Model: "unet" ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃ ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ │ input_layer │ (None, 128, 128, │ 0 │ - │ │ (InputLayer) │ 3) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d (Conv2D) │ (None, 128, 128, │ 1,792 │ input_layer[0][0] │ │ │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalization │ (None, 128, 128, │ 256 │ conv2d[0][0] │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu │ (None, 128, 128, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_1 (Conv2D) │ (None, 128, 128, │ 36,928 │ leaky_re_lu[0][0] │ │ │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 128, 128, │ 256 │ conv2d_1[0][0] │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_1 │ (None, 128, 128, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ max_pooling2d │ (None, 64, 64, │ 0 │ leaky_re_lu_1[0]… │ │ (MaxPooling2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_2 (Conv2D) │ (None, 64, 64, │ 73,856 │ max_pooling2d[0]… │ │ │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 64, 64, │ 512 │ conv2d_2[0][0] │ │ (BatchNormalizatio… │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_2 │ (None, 64, 64, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_3 (Conv2D) │ (None, 64, 64, │ 147,584 │ leaky_re_lu_2[0]… │ │ │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 64, 64, │ 512 │ conv2d_3[0][0] │ │ (BatchNormalizatio… │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_3 │ (None, 64, 64, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ max_pooling2d_1 │ (None, 32, 32, │ 0 │ leaky_re_lu_3[0]… │ │ (MaxPooling2D) │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_4 (Conv2D) │ (None, 32, 32, │ 295,168 │ max_pooling2d_1[… │ │ │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 32, 32, │ 1,024 │ conv2d_4[0][0] │ │ (BatchNormalizatio… │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_4 │ (None, 32, 32, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_5 (Conv2D) │ (None, 32, 32, │ 590,080 │ leaky_re_lu_4[0]… │ │ │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 32, 32, │ 1,024 │ conv2d_5[0][0] │ │ (BatchNormalizatio… │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_5 │ (None, 32, 32, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ max_pooling2d_2 │ (None, 16, 16, │ 0 │ leaky_re_lu_5[0]… │ │ (MaxPooling2D) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_6 (Conv2D) │ (None, 16, 16, │ 1,180,160 │ max_pooling2d_2[… │ │ │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 16, 16, │ 2,048 │ conv2d_6[0][0] │ │ (BatchNormalizatio… │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_6 │ (None, 16, 16, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_7 (Conv2D) │ (None, 16, 16, │ 2,359,808 │ leaky_re_lu_6[0]… │ │ │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 16, 16, │ 2,048 │ conv2d_7[0][0] │ │ (BatchNormalizatio… │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_7 │ (None, 16, 16, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ max_pooling2d_3 │ (None, 8, 8, 512) │ 0 │ leaky_re_lu_7[0]… │ │ (MaxPooling2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_8 (Conv2D) │ (None, 8, 8, │ 4,719,616 │ max_pooling2d_3[… │ │ │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 8, 8, │ 4,096 │ conv2d_8[0][0] │ │ (BatchNormalizatio… │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_8 │ (None, 8, 8, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_9 (Conv2D) │ (None, 8, 8, │ 9,438,208 │ leaky_re_lu_8[0]… │ │ │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 8, 8, │ 4,096 │ conv2d_9[0][0] │ │ (BatchNormalizatio… │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_9 │ (None, 8, 8, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ up_sampling2d │ (None, 16, 16, │ 0 │ leaky_re_lu_9[0]… │ │ (UpSampling2D) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ concatenate │ (None, 16, 16, │ 0 │ up_sampling2d[0]… │ │ (Concatenate) │ 1536) │ │ leaky_re_lu_7[0]… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_10 (Conv2D) │ (None, 16, 16, │ 7,078,400 │ concatenate[0][0] │ │ │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 16, 16, │ 2,048 │ conv2d_10[0][0] │ │ (BatchNormalizatio… │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_10 │ (None, 16, 16, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_11 (Conv2D) │ (None, 16, 16, │ 2,359,808 │ leaky_re_lu_10[0… │ │ │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 16, 16, │ 2,048 │ conv2d_11[0][0] │ │ (BatchNormalizatio… │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_11 │ (None, 16, 16, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ up_sampling2d_1 │ (None, 32, 32, │ 0 │ leaky_re_lu_11[0… │ │ (UpSampling2D) │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ concatenate_1 │ (None, 32, 32, │ 0 │ up_sampling2d_1[… │ │ (Concatenate) │ 768) │ │ leaky_re_lu_5[0]… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_12 (Conv2D) │ (None, 32, 32, │ 1,769,728 │ concatenate_1[0]… │ │ │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 32, 32, │ 1,024 │ conv2d_12[0][0] │ │ (BatchNormalizatio… │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_12 │ (None, 32, 32, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_13 (Conv2D) │ (None, 32, 32, │ 590,080 │ leaky_re_lu_12[0… │ │ │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 32, 32, │ 1,024 │ conv2d_13[0][0] │ │ (BatchNormalizatio… │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_13 │ (None, 32, 32, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ up_sampling2d_2 │ (None, 64, 64, │ 0 │ leaky_re_lu_13[0… │ │ (UpSampling2D) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ concatenate_2 │ (None, 64, 64, │ 0 │ up_sampling2d_2[… │ │ (Concatenate) │ 384) │ │ leaky_re_lu_3[0]… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_14 (Conv2D) │ (None, 64, 64, │ 442,496 │ concatenate_2[0]… │ │ │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 64, 64, │ 512 │ conv2d_14[0][0] │ │ (BatchNormalizatio… │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_14 │ (None, 64, 64, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_15 (Conv2D) │ (None, 64, 64, │ 147,584 │ leaky_re_lu_14[0… │ │ │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 64, 64, │ 512 │ conv2d_15[0][0] │ │ (BatchNormalizatio… │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_15 │ (None, 64, 64, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ up_sampling2d_3 │ (None, 128, 128, │ 0 │ leaky_re_lu_15[0… │ │ (UpSampling2D) │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ concatenate_3 │ (None, 128, 128, │ 0 │ up_sampling2d_3[… │ │ (Concatenate) │ 192) │ │ leaky_re_lu_1[0]… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_16 (Conv2D) │ (None, 128, 128, │ 110,656 │ concatenate_3[0]… │ │ │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 128, 128, │ 256 │ conv2d_16[0][0] │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_16 │ (None, 128, 128, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_17 (Conv2D) │ (None, 128, 128, │ 36,928 │ leaky_re_lu_16[0… │ │ │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 128, 128, │ 256 │ conv2d_17[0][0] │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_17 │ (None, 128, 128, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ dropout (Dropout) │ (None, 128, 128, │ 0 │ leaky_re_lu_17[0… │ │ │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_18 (Conv2D) │ (None, 128, 128, │ 130 │ dropout[0][0] │ │ │ 2) │ │ │ └─────────────────────┴───────────────────┴────────────┴───────────────────┘ Total params: 31,402,562 (119.79 MB) Trainable params: 31,390,786 (119.75 MB) Non-trainable params: 11,776 (46.00 KB) Epoch 1/10  1/120 ━━━━━━━━━━━━━━━━━━━━ 10:44 5s/step - accuracy: 0.6397 - loss: 0.7025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3204  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.5997 - loss: 0.7453 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3284  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.6020 - loss: 0.7351 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3274  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.6105 - loss: 0.7202 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3265  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.6225 - loss: 0.7031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3252  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.6359 - loss: 0.6860 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3240  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.6494 - loss: 0.6695 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3230  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 0.6622 - loss: 0.6542 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3224  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.6741 - loss: 0.6400 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3220  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.6853 - loss: 0.6267 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3218  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.6954 - loss: 0.6145 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3218  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.7049 - loss: 0.6030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3220  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.7137 - loss: 0.5922 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3223  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.7218 - loss: 0.5821 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3227  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.7294 - loss: 0.5726 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3232  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.7366 - loss: 0.5635 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3237  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.7433 - loss: 0.5548 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3244  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.7495 - loss: 0.5466 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3251  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.7555 - loss: 0.5387 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3258  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.7610 - loss: 0.5311 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3267  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.7663 - loss: 0.5239 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3275  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.7713 - loss: 0.5169 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3284  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.7759 - loss: 0.5103 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3293  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.7804 - loss: 0.5040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3302  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.7846 - loss: 0.4979 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3312  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.7886 - loss: 0.4920 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3321  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.7924 - loss: 0.4864 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3331  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.7960 - loss: 0.4810 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3341  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.7995 - loss: 0.4758 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3350  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.8028 - loss: 0.4707 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3360  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.8059 - loss: 0.4658 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3370  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.8090 - loss: 0.4610 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3379  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.8119 - loss: 0.4564 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3389  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.8146 - loss: 0.4520 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3399  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.8173 - loss: 0.4477 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3408  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.8199 - loss: 0.4436 - mean_absolute_error: 0.5000 - 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━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.8373 - loss: 0.4145 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3490  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.8391 - loss: 0.4113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3499  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.8409 - loss: 0.4082 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3507  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.8426 - loss: 0.4052 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3516  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.8443 - loss: 0.4022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3524  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.8460 - loss: 0.3994 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3532  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.8475 - loss: 0.3966 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3540  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - 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━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.8743 - loss: 0.3466 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3699  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.8752 - loss: 0.3449 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3704  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.8760 - loss: 0.3432 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3710  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.8768 - loss: 0.3415 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3716  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.8776 - loss: 0.3399 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3721  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.8784 - loss: 0.3383 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3727  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.8792 - loss: 0.3367 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3732  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - 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━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.8938 - loss: 0.3063 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3839 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.8943 - loss: 0.3052 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3843 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.8948 - loss: 0.3040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3847 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.8954 - loss: 0.3030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3851 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.8958 - loss: 0.3019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3855 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.8963 - loss: 0.3008 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3859 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.8968 - loss: 0.2997 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3862 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.8973 - loss: 0.2987 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3866 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.8978 - loss: 0.2977 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3870 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.8982 - loss: 0.2967 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3874 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.8987 - loss: 0.2957 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3877 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.8991 - loss: 0.2947 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3881 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.8996 - loss: 0.2937 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3884 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9000 - loss: 0.2927 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3888 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9004 - loss: 0.2917 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3891 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9009 - loss: 0.2908 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3895 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9013 - loss: 0.2898 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3898 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9017 - loss: 0.2889 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3902 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9021 - loss: 0.2880 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3905 120/120 ━━━━━━━━━━━━━━━━━━━━ 41s 302ms/step - accuracy: 0.9507 - loss: 0.1784 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4307 - val_accuracy: 0.9608 - val_loss: 0.2002 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4492 Epoch 2/10  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9698 - loss: 0.1099 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4630  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9690 - loss: 0.1118 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4635  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9692 - loss: 0.1111 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4638  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9699 - loss: 0.1089 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4643  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9704 - loss: 0.1073 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4646  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9708 - loss: 0.1061 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4649  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9711 - loss: 0.1050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4650  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9715 - loss: 0.1039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4652  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9718 - loss: 0.1030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4654  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9720 - loss: 0.1024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4656  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9721 - loss: 0.1021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4656  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9722 - loss: 0.1018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4657  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9722 - loss: 0.1016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4658  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9722 - loss: 0.1015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4658  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9722 - loss: 0.1014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4659  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9723 - loss: 0.1012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4659  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9723 - loss: 0.1010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4660  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9724 - loss: 0.1009 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4660  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9724 - loss: 0.1007 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4660  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9725 - loss: 0.1005 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4660  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9725 - loss: 0.1003 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4661  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9726 - loss: 0.1002 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4661  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9726 - loss: 0.1000 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4661  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9726 - loss: 0.0999 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4662  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9727 - loss: 0.0998 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4662  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9727 - loss: 0.0998 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4662  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9727 - loss: 0.0997 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4662  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9727 - loss: 0.0996 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4663  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9727 - loss: 0.0995 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4663  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9728 - loss: 0.0994 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4663  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9728 - loss: 0.0993 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4663  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9728 - loss: 0.0992 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4664  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9728 - loss: 0.0991 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4664  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9729 - loss: 0.0990 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4664  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9729 - loss: 0.0989 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4665  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9729 - loss: 0.0988 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4665  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9729 - loss: 0.0987 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4665  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9729 - loss: 0.0986 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4665  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9729 - loss: 0.0986 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4666  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9729 - loss: 0.0985 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4666  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9730 - loss: 0.0984 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4666  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9730 - loss: 0.0983 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4666  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9730 - loss: 0.0983 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4667  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9730 - loss: 0.0982 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4667  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9730 - loss: 0.0981 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4667  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9730 - loss: 0.0980 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4668  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9731 - loss: 0.0979 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4668  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9731 - loss: 0.0978 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4668  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9731 - loss: 0.0978 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4668  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9731 - loss: 0.0977 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4668  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9731 - loss: 0.0976 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4669  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9731 - loss: 0.0976 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4669  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9731 - loss: 0.0975 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4669  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9731 - loss: 0.0974 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4669  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9732 - loss: 0.0974 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4670  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9732 - loss: 0.0973 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4670  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9732 - loss: 0.0972 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4670  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9732 - loss: 0.0971 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4671  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9732 - loss: 0.0971 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4671  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9732 - loss: 0.0970 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4671  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9732 - loss: 0.0969 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4671  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9733 - loss: 0.0968 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4671  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9733 - loss: 0.0968 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4672  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9733 - loss: 0.0967 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4672  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9733 - loss: 0.0967 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4672  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9733 - loss: 0.0966 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4672  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9733 - loss: 0.0965 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4673  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9733 - loss: 0.0964 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4673  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9733 - loss: 0.0964 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4673  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9734 - loss: 0.0963 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4673  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9734 - loss: 0.0962 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4674  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9734 - loss: 0.0962 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4674  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9734 - loss: 0.0961 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4674  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9734 - loss: 0.0960 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4674  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9734 - loss: 0.0960 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4675  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9734 - loss: 0.0959 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4675  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9734 - loss: 0.0959 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4675  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9734 - loss: 0.0958 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4675  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9735 - loss: 0.0957 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4676  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9735 - loss: 0.0957 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4676  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9735 - loss: 0.0956 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4676  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9735 - loss: 0.0955 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4676  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9735 - loss: 0.0955 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4677  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9735 - loss: 0.0954 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4677  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9735 - loss: 0.0954 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4677  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9735 - loss: 0.0953 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4677  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9735 - loss: 0.0953 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4677   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9736 - loss: 0.0952 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4678  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9736 - loss: 0.0951 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4678  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9736 - loss: 0.0951 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4678  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9736 - loss: 0.0950 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4678  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9736 - loss: 0.0950 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4679  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9736 - loss: 0.0949 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4679  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9736 - loss: 0.0948 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4679  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9736 - loss: 0.0948 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4679  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9736 - loss: 0.0947 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4679  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9736 - loss: 0.0947 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4680  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9737 - loss: 0.0946 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4680  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9737 - loss: 0.0946 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4680 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9737 - loss: 0.0945 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4680 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9737 - loss: 0.0945 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4680 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9737 - loss: 0.0944 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4681 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9737 - loss: 0.0944 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4681 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9737 - loss: 0.0943 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4681 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9737 - loss: 0.0943 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4681 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9737 - loss: 0.0942 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4682 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9737 - loss: 0.0941 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4682 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9737 - loss: 0.0941 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4682 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9738 - loss: 0.0940 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4682 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9738 - loss: 0.0940 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4682 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9738 - loss: 0.0939 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4683 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9738 - loss: 0.0939 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4683 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9738 - loss: 0.0938 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4683 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9738 - loss: 0.0938 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4683 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9738 - loss: 0.0937 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4683 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9738 - loss: 0.0937 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4684 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9738 - loss: 0.0936 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4684 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9738 - loss: 0.0936 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4684 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9738 - loss: 0.0935 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4684 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9739 - loss: 0.0935 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4684 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9749 - loss: 0.0875 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4709 - val_accuracy: 0.9609 - val_loss: 0.1615 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4827 Epoch 3/10  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9726 - loss: 0.0886 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4741  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9723 - loss: 0.0902 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4745  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9725 - loss: 0.0900 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4746  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9732 - loss: 0.0882 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4749  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9736 - loss: 0.0869 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4752  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9741 - loss: 0.0858 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4753  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9744 - loss: 0.0849 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4753  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9747 - loss: 0.0841 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4754  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9749 - loss: 0.0833 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9751 - loss: 0.0829 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 0.9752 - loss: 0.0826 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 0.9753 - loss: 0.0824 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 0.9753 - loss: 0.0822 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 0.9753 - loss: 0.0822 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 0.9753 - loss: 0.0822 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 0.9754 - loss: 0.0821 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9754 - loss: 0.0820 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9755 - loss: 0.0819 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9755 - loss: 0.0818 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9755 - loss: 0.0817 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 0.9756 - loss: 0.0815 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 0.9756 - loss: 0.0814 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 0.9756 - loss: 0.0813 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9757 - loss: 0.0812 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9757 - loss: 0.0812 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9757 - loss: 0.0811 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9757 - loss: 0.0811 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9757 - loss: 0.0810 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9757 - loss: 0.0810 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9758 - loss: 0.0809 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 0.9758 - loss: 0.0808 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 0.9758 - loss: 0.0807 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 0.9758 - loss: 0.0807 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 0.9758 - loss: 0.0806 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 0.9758 - loss: 0.0805 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 0.9759 - loss: 0.0804 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9759 - loss: 0.0804 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9759 - loss: 0.0803 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9759 - loss: 0.0803 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9759 - loss: 0.0802 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9759 - loss: 0.0802 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9759 - loss: 0.0801 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9759 - loss: 0.0801 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9760 - loss: 0.0800 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9760 - loss: 0.0799 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9760 - loss: 0.0799 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4757  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9760 - loss: 0.0798 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4757  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9760 - loss: 0.0797 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4757  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9760 - loss: 0.0797 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4757  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9760 - loss: 0.0796 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4757  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9761 - loss: 0.0796 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4757  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9761 - loss: 0.0795 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4757  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9761 - loss: 0.0795 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4757  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9761 - loss: 0.0794 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4757  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9761 - loss: 0.0793 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4758  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9761 - loss: 0.0793 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4758  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9761 - loss: 0.0792 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4758  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9761 - loss: 0.0792 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4758  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9761 - loss: 0.0791 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4758  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9762 - loss: 0.0790 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4758  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9762 - loss: 0.0790 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4758  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9762 - loss: 0.0789 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4758  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9762 - loss: 0.0789 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4759  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9762 - loss: 0.0788 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4759  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9762 - loss: 0.0788 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4759  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9762 - loss: 0.0787 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4759  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9762 - loss: 0.0787 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4759  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9762 - loss: 0.0786 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4759  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9763 - loss: 0.0786 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4759  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9763 - loss: 0.0785 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4759  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9763 - loss: 0.0785 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9763 - loss: 0.0784 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9763 - loss: 0.0784 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9763 - loss: 0.0784 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9763 - loss: 0.0783 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9763 - loss: 0.0783 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9763 - loss: 0.0782 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9763 - loss: 0.0782 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9763 - loss: 0.0781 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9763 - loss: 0.0781 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9764 - loss: 0.0780 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9764 - loss: 0.0780 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9764 - loss: 0.0780 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9764 - loss: 0.0779 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9764 - loss: 0.0779 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9764 - loss: 0.0778 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9764 - loss: 0.0778 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9764 - loss: 0.0778 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9764 - loss: 0.0777 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9764 - loss: 0.0777 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9764 - loss: 0.0776 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9764 - loss: 0.0776 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9764 - loss: 0.0775 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9765 - loss: 0.0775 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9765 - loss: 0.0775 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9765 - loss: 0.0774 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9765 - loss: 0.0774 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9765 - loss: 0.0774 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9765 - loss: 0.0773 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9765 - loss: 0.0773 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9765 - loss: 0.0772 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9765 - loss: 0.0772 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9765 - loss: 0.0772 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9765 - loss: 0.0771 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9765 - loss: 0.0771 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9765 - loss: 0.0771 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9765 - loss: 0.0770 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9766 - loss: 0.0770 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9766 - loss: 0.0769 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9766 - loss: 0.0769 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9766 - loss: 0.0769 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9766 - loss: 0.0768 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9766 - loss: 0.0768 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9766 - loss: 0.0768 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9766 - loss: 0.0767 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9766 - loss: 0.0767 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9766 - loss: 0.0767 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4765 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9766 - loss: 0.0766 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4765 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9766 - loss: 0.0766 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4765 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9766 - loss: 0.0766 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4765 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9774 - loss: 0.0724 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4777 - val_accuracy: 0.9609 - val_loss: 0.1614 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4883 Epoch 4/10  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9745 - loss: 0.0749 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4777  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9742 - loss: 0.0765 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4781  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9743 - loss: 0.0765 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4783  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9750 - loss: 0.0747 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4787  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9755 - loss: 0.0734 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4789  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9759 - loss: 0.0723 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4791  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 0.9762 - loss: 0.0714 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4792  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9766 - loss: 0.0705 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4793  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9768 - loss: 0.0697 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4795  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9771 - loss: 0.0691 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4796  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9772 - loss: 0.0688 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4796  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9773 - loss: 0.0685 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4797  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9774 - loss: 0.0682 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4797  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9775 - loss: 0.0681 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4797  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9775 - loss: 0.0680 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4797  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9776 - loss: 0.0679 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4797  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9776 - loss: 0.0677 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4797  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9777 - loss: 0.0676 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4798  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9777 - loss: 0.0674 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4798  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9778 - loss: 0.0673 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4798  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9778 - loss: 0.0671 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4798  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9779 - loss: 0.0669 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 0.9779 - loss: 0.0668 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9780 - loss: 0.0667 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9780 - loss: 0.0666 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9780 - loss: 0.0666 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9780 - loss: 0.0665 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9781 - loss: 0.0664 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9781 - loss: 0.0664 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9781 - loss: 0.0663 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9781 - loss: 0.0662 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9782 - loss: 0.0661 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9782 - loss: 0.0661 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9782 - loss: 0.0660 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9782 - loss: 0.0659 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9783 - loss: 0.0659 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9783 - loss: 0.0658 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9783 - loss: 0.0658 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9783 - loss: 0.0658 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9783 - loss: 0.0657 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9783 - loss: 0.0657 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9783 - loss: 0.0656 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9783 - loss: 0.0656 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9784 - loss: 0.0655 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9784 - loss: 0.0655 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9784 - loss: 0.0654 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9784 - loss: 0.0654 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9784 - loss: 0.0654 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9784 - loss: 0.0653 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9784 - loss: 0.0653 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9784 - loss: 0.0653 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9785 - loss: 0.0652 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9785 - loss: 0.0652 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9785 - loss: 0.0652 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9785 - loss: 0.0652 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9785 - loss: 0.0651 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9785 - loss: 0.0651 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9785 - loss: 0.0651 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9785 - loss: 0.0650 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9785 - loss: 0.0650 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9785 - loss: 0.0650 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9785 - loss: 0.0650 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9785 - loss: 0.0650 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9786 - loss: 0.0649 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9786 - loss: 0.0649 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9786 - loss: 0.0649 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9786 - loss: 0.0649 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9786 - loss: 0.0648 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9786 - loss: 0.0648 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9786 - loss: 0.0648 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9786 - loss: 0.0648 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9786 - loss: 0.0648 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9786 - loss: 0.0647 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9786 - loss: 0.0647 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9786 - loss: 0.0647 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9786 - loss: 0.0647 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9786 - loss: 0.0647 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9786 - loss: 0.0647 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9786 - loss: 0.0647 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9786 - loss: 0.0646 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9787 - loss: 0.0646 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9787 - loss: 0.0646 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9787 - loss: 0.0646 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9787 - loss: 0.0646 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9787 - loss: 0.0645 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9787 - loss: 0.0645 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9787 - loss: 0.0645 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9787 - loss: 0.0645 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9787 - loss: 0.0645 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9787 - loss: 0.0645 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9787 - loss: 0.0645 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9787 - loss: 0.0644 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9787 - loss: 0.0644 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9787 - loss: 0.0644 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9787 - loss: 0.0644 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9787 - loss: 0.0644 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9787 - loss: 0.0644 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9787 - loss: 0.0643 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9787 - loss: 0.0643 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9787 - loss: 0.0643 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9787 - loss: 0.0643 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9787 - loss: 0.0643 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9787 - loss: 0.0643 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9788 - loss: 0.0642 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9788 - loss: 0.0642 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9788 - loss: 0.0642 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9788 - loss: 0.0642 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9788 - loss: 0.0642 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9788 - loss: 0.0642 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9788 - loss: 0.0642 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9788 - loss: 0.0641 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9788 - loss: 0.0641 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9788 - loss: 0.0641 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9788 - loss: 0.0641 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9788 - loss: 0.0641 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9788 - loss: 0.0641 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9788 - loss: 0.0640 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9788 - loss: 0.0640 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9788 - loss: 0.0640 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9788 - loss: 0.0640 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 0.9793 - loss: 0.0621 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4812 - val_accuracy: 0.9709 - val_loss: 0.1186 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4897 Epoch 5/10  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9753 - loss: 0.0682 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4777  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9751 - loss: 0.0692 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4783  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 0.9754 - loss: 0.0688 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4787  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 0.9761 - loss: 0.0671 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4794  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 0.9766 - loss: 0.0659 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4798  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 0.9770 - loss: 0.0649 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 0.9774 - loss: 0.0641 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9777 - loss: 0.0632 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4806  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9780 - loss: 0.0625 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4808  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9782 - loss: 0.0621 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4810  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9784 - loss: 0.0618 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4811  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9785 - loss: 0.0616 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4812  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9786 - loss: 0.0614 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4812  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9786 - loss: 0.0613 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4813  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9786 - loss: 0.0613 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4813  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9787 - loss: 0.0612 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4814  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9787 - loss: 0.0612 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4814  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9788 - loss: 0.0611 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4814  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9788 - loss: 0.0610 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4815  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9789 - loss: 0.0609 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4815  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9789 - loss: 0.0608 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4815  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9790 - loss: 0.0607 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4816  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9790 - loss: 0.0606 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4816  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9790 - loss: 0.0605 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4816  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9791 - loss: 0.0605 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4816  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9791 - loss: 0.0605 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4816  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9791 - loss: 0.0605 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4816  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9791 - loss: 0.0604 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4817  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9791 - loss: 0.0604 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4817  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9791 - loss: 0.0604 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4817  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9792 - loss: 0.0603 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4817  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9792 - loss: 0.0603 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4817  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9792 - loss: 0.0602 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4817  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9792 - loss: 0.0602 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4818  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9792 - loss: 0.0601 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4818  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9793 - loss: 0.0601 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4818  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9793 - loss: 0.0601 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4818  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9793 - loss: 0.0601 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4818  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9793 - loss: 0.0600 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4818  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9793 - loss: 0.0600 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4818  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9793 - loss: 0.0600 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4818  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9793 - loss: 0.0600 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4818  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9793 - loss: 0.0599 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4818  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9794 - loss: 0.0599 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4819  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9794 - loss: 0.0599 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4819  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9794 - loss: 0.0598 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4819  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9794 - loss: 0.0598 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4819  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9794 - loss: 0.0598 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4819  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9794 - loss: 0.0597 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4819  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9794 - loss: 0.0597 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4819  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9794 - loss: 0.0597 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4819  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9794 - loss: 0.0597 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4819  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9795 - loss: 0.0597 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9795 - loss: 0.0597 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9795 - loss: 0.0596 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9795 - loss: 0.0596 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9795 - loss: 0.0596 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9795 - loss: 0.0595 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9795 - loss: 0.0595 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9795 - loss: 0.0595 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9795 - loss: 0.0595 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9795 - loss: 0.0595 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9795 - loss: 0.0594 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9795 - loss: 0.0594 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9796 - loss: 0.0594 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9796 - loss: 0.0594 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9796 - loss: 0.0594 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9796 - loss: 0.0593 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9796 - loss: 0.0593 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9796 - loss: 0.0593 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9796 - loss: 0.0593 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9796 - loss: 0.0593 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9796 - loss: 0.0592 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9796 - loss: 0.0592 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9796 - loss: 0.0592 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9796 - loss: 0.0592 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9796 - loss: 0.0592 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9796 - loss: 0.0592 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9797 - loss: 0.0591 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9797 - loss: 0.0591 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9797 - loss: 0.0591 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9797 - loss: 0.0591 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9797 - loss: 0.0591 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9797 - loss: 0.0590 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9797 - loss: 0.0590 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9797 - loss: 0.0590 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9797 - loss: 0.0590 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9797 - loss: 0.0590 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9797 - loss: 0.0589 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9797 - loss: 0.0589 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9797 - loss: 0.0589 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9797 - loss: 0.0589 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9797 - loss: 0.0589 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9797 - loss: 0.0589 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9797 - loss: 0.0588 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9798 - loss: 0.0588 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9798 - loss: 0.0588 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9798 - loss: 0.0588 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9798 - loss: 0.0588 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9798 - loss: 0.0588 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9798 - loss: 0.0588 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9798 - loss: 0.0588 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9798 - loss: 0.0587 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9798 - loss: 0.0587 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9798 - loss: 0.0587 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9798 - loss: 0.0587 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9798 - loss: 0.0587 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9798 - loss: 0.0587 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9798 - loss: 0.0587 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9798 - loss: 0.0587 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 0.9798 - loss: 0.0587 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 0.9798 - loss: 0.0587 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 0.9798 - loss: 0.0586 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 0.9798 - loss: 0.0586 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 0.9798 - loss: 0.0586 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 0.9798 - loss: 0.0586 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9798 - loss: 0.0586 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9798 - loss: 0.0586 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9798 - loss: 0.0586 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9798 - loss: 0.0586 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824 120/120 ━━━━━━━━━━━━━━━━━━━━ 37s 305ms/step - accuracy: 0.9802 - loss: 0.0578 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4828 - val_accuracy: 0.9744 - val_loss: 0.0919 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4884 Epoch 6/10  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9761 - loss: 0.0668 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9758 - loss: 0.0680 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9760 - loss: 0.0677 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9767 - loss: 0.0661 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4808  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9772 - loss: 0.0650 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4810  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9775 - loss: 0.0641 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4812  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9778 - loss: 0.0634 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4814  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 0.9782 - loss: 0.0626 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4816  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 0.9784 - loss: 0.0619 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4817  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 0.9786 - loss: 0.0614 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4819  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 0.9788 - loss: 0.0611 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 0.9789 - loss: 0.0608 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 0.9790 - loss: 0.0606 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 0.9790 - loss: 0.0605 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 0.9790 - loss: 0.0604 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 0.9791 - loss: 0.0603 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9791 - loss: 0.0601 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9792 - loss: 0.0600 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9792 - loss: 0.0598 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9793 - loss: 0.0597 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9794 - loss: 0.0595 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9794 - loss: 0.0594 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9795 - loss: 0.0592 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9795 - loss: 0.0591 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9795 - loss: 0.0590 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9796 - loss: 0.0589 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9796 - loss: 0.0589 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9796 - loss: 0.0588 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9796 - loss: 0.0587 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4827  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9797 - loss: 0.0586 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4827  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 0.9797 - loss: 0.0585 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4827  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 0.9797 - loss: 0.0584 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4827  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 0.9798 - loss: 0.0583 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4827  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 0.9798 - loss: 0.0582 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4828  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 0.9798 - loss: 0.0582 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4828  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 0.9798 - loss: 0.0581 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4828  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9799 - loss: 0.0580 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4828  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9799 - loss: 0.0579 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4828  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9799 - loss: 0.0579 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4828  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9799 - loss: 0.0578 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4829  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9799 - loss: 0.0577 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4829  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9800 - loss: 0.0577 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4829  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9800 - loss: 0.0576 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4829  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9800 - loss: 0.0575 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4829  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9800 - loss: 0.0575 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4829  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9800 - loss: 0.0574 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4830  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9801 - loss: 0.0573 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4830  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9801 - loss: 0.0573 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4830  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9801 - loss: 0.0572 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4830  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9801 - loss: 0.0572 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4830  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9801 - loss: 0.0571 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4830  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9801 - loss: 0.0571 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4830  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9801 - loss: 0.0570 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4830  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9802 - loss: 0.0569 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4831  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9802 - loss: 0.0569 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4831  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9802 - loss: 0.0568 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4831  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9802 - loss: 0.0568 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4831  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9802 - loss: 0.0567 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4831  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9802 - loss: 0.0567 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4831  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9803 - loss: 0.0566 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4831  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9803 - loss: 0.0566 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4831  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 0.9803 - loss: 0.0565 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4831  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 0.9803 - loss: 0.0565 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4832  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 0.9803 - loss: 0.0564 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4832  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 0.9803 - loss: 0.0564 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4832  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 0.9803 - loss: 0.0563 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4832  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 0.9803 - loss: 0.0563 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4832  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 0.9803 - loss: 0.0562 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4832  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 0.9804 - loss: 0.0562 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4832  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 0.9804 - loss: 0.0561 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4832  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 0.9804 - loss: 0.0561 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4832  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 0.9804 - loss: 0.0561 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4832  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 0.9804 - loss: 0.0560 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4833  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 301ms/step - accuracy: 0.9804 - loss: 0.0560 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4833  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 301ms/step - accuracy: 0.9804 - loss: 0.0559 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4833  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 301ms/step - accuracy: 0.9804 - loss: 0.0559 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4833  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 0.9805 - loss: 0.0558 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4833  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 0.9805 - loss: 0.0558 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4833  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 0.9805 - loss: 0.0558 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4833  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 0.9805 - loss: 0.0557 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4833  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 0.9805 - loss: 0.0557 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4833  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 0.9805 - loss: 0.0556 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4833  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 0.9805 - loss: 0.0556 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4833  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 0.9805 - loss: 0.0556 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4834  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 0.9805 - loss: 0.0555 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4834  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 0.9805 - loss: 0.0555 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4834  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9805 - loss: 0.0555 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4834   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9806 - loss: 0.0554 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4834  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9806 - loss: 0.0554 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4834  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9806 - loss: 0.0554 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4834  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 0.9806 - loss: 0.0553 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4834  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 0.9806 - loss: 0.0553 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4834  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 0.9806 - loss: 0.0553 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4834  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 0.9806 - loss: 0.0552 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4834  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 0.9806 - loss: 0.0552 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4834  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 0.9806 - loss: 0.0552 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9806 - loss: 0.0551 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9806 - loss: 0.0551 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9806 - loss: 0.0551 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9807 - loss: 0.0550 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 0.9807 - loss: 0.0550 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 0.9807 - loss: 0.0550 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 0.9807 - loss: 0.0549 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 0.9807 - loss: 0.0549 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 0.9807 - loss: 0.0549 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 0.9807 - loss: 0.0548 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9807 - loss: 0.0548 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9807 - loss: 0.0548 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9807 - loss: 0.0548 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9807 - loss: 0.0547 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4836 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 0.9807 - loss: 0.0547 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4836 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 0.9807 - loss: 0.0547 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4836 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 0.9807 - loss: 0.0547 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4836 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 0.9807 - loss: 0.0546 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4836 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 0.9808 - loss: 0.0546 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4836 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 0.9808 - loss: 0.0546 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4836 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9808 - loss: 0.0545 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4836 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9808 - loss: 0.0545 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4836 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9808 - loss: 0.0545 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4836 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9808 - loss: 0.0545 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4836 120/120 ━━━━━━━━━━━━━━━━━━━━ 37s 305ms/step - accuracy: 0.9815 - loss: 0.0513 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844 - val_accuracy: 0.9770 - val_loss: 0.0748 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4897 Epoch 7/10  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9785 - loss: 0.0566 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9781 - loss: 0.0576 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9783 - loss: 0.0572 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4836  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9789 - loss: 0.0557 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4839  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9794 - loss: 0.0546 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4841  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9797 - loss: 0.0538 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9800 - loss: 0.0531 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 0.9803 - loss: 0.0524 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 0.9805 - loss: 0.0518 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 0.9807 - loss: 0.0514 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4846  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 0.9808 - loss: 0.0511 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4847  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 0.9810 - loss: 0.0508 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4848  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 0.9810 - loss: 0.0507 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4848  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 0.9811 - loss: 0.0506 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4848  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 0.9811 - loss: 0.0505 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4848  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 0.9811 - loss: 0.0504 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4849  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9812 - loss: 0.0503 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4849  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9812 - loss: 0.0502 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4849  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9813 - loss: 0.0500 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4849  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9813 - loss: 0.0499 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4849  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 0.9814 - loss: 0.0498 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4850  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 0.9814 - loss: 0.0497 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4850  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 0.9814 - loss: 0.0496 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4850  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9815 - loss: 0.0495 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4850  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - 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mean_squared_error: 0.4852  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 0.9818 - loss: 0.0486 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4852  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 0.9818 - loss: 0.0486 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4852  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 0.9818 - loss: 0.0486 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4852  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 0.9818 - loss: 0.0485 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4852  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 0.9818 - loss: 0.0485 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4852  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 0.9818 - loss: 0.0484 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4852  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 0.9818 - loss: 0.0484 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4852  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 0.9819 - loss: 0.0484 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 0.9819 - loss: 0.0483 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 0.9819 - loss: 0.0483 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 0.9819 - loss: 0.0483 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 0.9819 - loss: 0.0483 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 0.9819 - loss: 0.0482 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 0.9819 - loss: 0.0482 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 0.9819 - loss: 0.0482 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 0.9819 - loss: 0.0481 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 0.9819 - loss: 0.0481 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 0.9819 - loss: 0.0481 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 0.9820 - loss: 0.0481 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 0.9820 - loss: 0.0480 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 0.9820 - loss: 0.0480 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 0.9820 - loss: 0.0480 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 0.9820 - loss: 0.0480 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 0.9820 - loss: 0.0479 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 0.9820 - loss: 0.0479 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 0.9820 - loss: 0.0479 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 0.9820 - loss: 0.0479 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 0.9820 - loss: 0.0479 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 0.9820 - loss: 0.0478 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 0.9820 - loss: 0.0478 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 0.9820 - loss: 0.0478 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 0.9820 - loss: 0.0478 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 0.9821 - loss: 0.0478 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 0.9821 - loss: 0.0477 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 301ms/step - accuracy: 0.9821 - loss: 0.0477 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 301ms/step - accuracy: 0.9821 - loss: 0.0477 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 301ms/step - accuracy: 0.9821 - loss: 0.0477 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 0.9821 - loss: 0.0477 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 0.9821 - loss: 0.0477 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 0.9821 - loss: 0.0477 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 0.9821 - loss: 0.0476 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 0.9821 - loss: 0.0476 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 0.9821 - loss: 0.0476 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 0.9821 - loss: 0.0476 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 0.9821 - loss: 0.0476 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 0.9821 - loss: 0.0476 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 0.9821 - loss: 0.0475 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9821 - loss: 0.0475 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9821 - loss: 0.0475 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9821 - loss: 0.0475 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9821 - loss: 0.0475 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9821 - loss: 0.0475 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9821 - loss: 0.0475 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9821 - loss: 0.0475 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9821 - loss: 0.0475 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9821 - loss: 0.0474 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9822 - loss: 0.0474 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9822 - loss: 0.0474 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9822 - loss: 0.0474 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9822 - loss: 0.0474 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9822 - loss: 0.0474 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9822 - loss: 0.0474 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9822 - loss: 0.0474 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9822 - loss: 0.0474 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9822 - loss: 0.0474 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9822 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9822 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9822 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9822 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9822 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9822 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9822 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9822 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9822 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9822 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9822 - loss: 0.0472 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9822 - loss: 0.0472 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9822 - loss: 0.0472 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9822 - loss: 0.0472 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4856 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9822 - loss: 0.0472 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4856 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9822 - loss: 0.0472 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4856 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 0.9826 - loss: 0.0461 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 - val_accuracy: 0.9773 - val_loss: 0.0708 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4886 Epoch 8/10  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9797 - loss: 0.0523 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4861  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 0.9793 - loss: 0.0529 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9794 - loss: 0.0526 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4857  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9799 - loss: 0.0514 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9803 - loss: 0.0504 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9806 - loss: 0.0496 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4860  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9809 - loss: 0.0490 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4860  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9812 - loss: 0.0483 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4861  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9814 - loss: 0.0477 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4862  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9816 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4863  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9817 - loss: 0.0470 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4863  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9818 - loss: 0.0467 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4863  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9819 - loss: 0.0465 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4863  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9819 - loss: 0.0464 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9820 - loss: 0.0463 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9820 - loss: 0.0462 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9821 - loss: 0.0461 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9821 - loss: 0.0459 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9822 - loss: 0.0458 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9822 - loss: 0.0457 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9823 - loss: 0.0456 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9823 - loss: 0.0455 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9823 - loss: 0.0454 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9824 - loss: 0.0453 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9824 - loss: 0.0452 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9824 - loss: 0.0451 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9825 - loss: 0.0451 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9825 - loss: 0.0450 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9825 - loss: 0.0450 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9825 - loss: 0.0449 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9825 - loss: 0.0448 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9826 - loss: 0.0448 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9826 - loss: 0.0447 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9826 - loss: 0.0446 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9826 - loss: 0.0446 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9827 - loss: 0.0445 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9827 - loss: 0.0445 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9827 - loss: 0.0444 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9827 - loss: 0.0444 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9827 - loss: 0.0444 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9827 - loss: 0.0443 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9828 - loss: 0.0443 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9828 - loss: 0.0442 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9828 - loss: 0.0442 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9828 - loss: 0.0441 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9828 - loss: 0.0441 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9828 - loss: 0.0441 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9828 - loss: 0.0440 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9829 - loss: 0.0440 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9829 - loss: 0.0440 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9829 - loss: 0.0440 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9829 - loss: 0.0439 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9829 - loss: 0.0439 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9829 - loss: 0.0439 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9829 - loss: 0.0438 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9829 - loss: 0.0438 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9829 - loss: 0.0438 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9830 - loss: 0.0438 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9830 - loss: 0.0437 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9830 - loss: 0.0437 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9830 - loss: 0.0437 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9830 - loss: 0.0437 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9830 - loss: 0.0436 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9830 - loss: 0.0436 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9830 - loss: 0.0436 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9830 - loss: 0.0436 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9830 - loss: 0.0436 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9830 - loss: 0.0435 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9830 - loss: 0.0435 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9830 - loss: 0.0435 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9830 - loss: 0.0435 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9831 - loss: 0.0435 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9831 - loss: 0.0434 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9831 - loss: 0.0434 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9831 - loss: 0.0434 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9831 - loss: 0.0434 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9831 - loss: 0.0434 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9831 - loss: 0.0434 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9831 - loss: 0.0433 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 0.9831 - loss: 0.0433 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 0.9831 - loss: 0.0433 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 0.9831 - loss: 0.0433 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 0.9831 - loss: 0.0433 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 0.9831 - loss: 0.0432 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 0.9831 - loss: 0.0432 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 0.9831 - loss: 0.0432 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9831 - loss: 0.0432 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9831 - loss: 0.0432 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9831 - loss: 0.0432 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9832 - loss: 0.0432 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 0.9832 - loss: 0.0431 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 0.9832 - loss: 0.0431 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 0.9832 - loss: 0.0431 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 0.9832 - loss: 0.0431 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 0.9832 - loss: 0.0431 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 0.9832 - loss: 0.0431 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9832 - loss: 0.0430 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9832 - loss: 0.0430 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9832 - loss: 0.0430 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9832 - loss: 0.0430 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 0.9832 - loss: 0.0430 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 0.9832 - loss: 0.0430 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 0.9832 - loss: 0.0430 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 0.9832 - loss: 0.0429 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 0.9832 - loss: 0.0429 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 0.9832 - loss: 0.0429 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9832 - loss: 0.0429 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9832 - loss: 0.0429 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9832 - loss: 0.0429 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9832 - loss: 0.0429 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 0.9832 - loss: 0.0428 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 0.9833 - loss: 0.0428 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 0.9833 - loss: 0.0428 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 0.9833 - loss: 0.0428 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 0.9833 - loss: 0.0428 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 0.9833 - loss: 0.0428 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9833 - loss: 0.0428 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9833 - loss: 0.0427 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9833 - loss: 0.0427 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9833 - loss: 0.0427 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868 120/120 ━━━━━━━━━━━━━━━━━━━━ 37s 305ms/step - accuracy: 0.9837 - loss: 0.0412 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4871 - val_accuracy: 0.9755 - val_loss: 0.0712 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4842 Epoch 9/10  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 307ms/step - accuracy: 0.9807 - loss: 0.0482 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9804 - loss: 0.0484 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9806 - loss: 0.0478 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4848  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9811 - loss: 0.0465 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9815 - loss: 0.0456 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4856  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9818 - loss: 0.0449 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 0.9820 - loss: 0.0443 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4860  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 0.9823 - loss: 0.0437 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4862  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 0.9825 - loss: 0.0432 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 0.9827 - loss: 0.0428 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 0.9828 - loss: 0.0426 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 302ms/step - accuracy: 0.9829 - loss: 0.0423 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 0.9830 - loss: 0.0422 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 0.9830 - loss: 0.0421 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 0.9830 - loss: 0.0420 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 0.9831 - loss: 0.0419 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  17/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 0.9831 - loss: 0.0418 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9832 - loss: 0.0418 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9832 - loss: 0.0417 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9832 - loss: 0.0416 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4870  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 0.9833 - loss: 0.0415 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4870  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 0.9833 - loss: 0.0414 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4870  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 0.9833 - loss: 0.0413 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4871  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9834 - loss: 0.0412 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4871  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9834 - loss: 0.0412 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4871  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9834 - loss: 0.0412 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4871  27/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9834 - loss: 0.0411 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4871  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9834 - loss: 0.0411 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4871  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9835 - loss: 0.0410 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4872  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9835 - loss: 0.0410 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4872  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 0.9835 - loss: 0.0410 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4872  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 0.9835 - loss: 0.0409 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4872  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 0.9835 - loss: 0.0409 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4872  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 0.9836 - loss: 0.0408 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4872  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 0.9836 - loss: 0.0408 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4872  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 0.9836 - loss: 0.0407 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4873  37/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 0.9836 - loss: 0.0407 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4873  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 0.9836 - loss: 0.0407 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4873  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 0.9836 - loss: 0.0407 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4873  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 0.9836 - loss: 0.0406 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4873  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 0.9836 - loss: 0.0406 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4873  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 0.9836 - loss: 0.0406 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4873  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 302ms/step - accuracy: 0.9837 - loss: 0.0405 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4873  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 302ms/step - accuracy: 0.9837 - loss: 0.0405 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4873  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 0.9837 - loss: 0.0405 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4873  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 0.9837 - loss: 0.0404 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4873  47/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 0.9837 - loss: 0.0404 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4874  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 0.9837 - loss: 0.0404 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4874  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 302ms/step - accuracy: 0.9837 - loss: 0.0403 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4874  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 0.9837 - loss: 0.0403 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4874  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 0.9838 - loss: 0.0403 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4874  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 302ms/step - accuracy: 0.9838 - loss: 0.0403 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4874  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 302ms/step - accuracy: 0.9838 - loss: 0.0403 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4874  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 0.9838 - loss: 0.0402 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4874  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 0.9838 - loss: 0.0402 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4874  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 302ms/step - accuracy: 0.9838 - loss: 0.0402 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4874  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 302ms/step - accuracy: 0.9838 - loss: 0.0402 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4874  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 302ms/step - accuracy: 0.9838 - loss: 0.0401 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4874  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 302ms/step - accuracy: 0.9838 - loss: 0.0401 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4874  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 302ms/step - accuracy: 0.9838 - loss: 0.0401 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4874  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 302ms/step - accuracy: 0.9838 - loss: 0.0401 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 302ms/step - accuracy: 0.9839 - loss: 0.0400 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 302ms/step - accuracy: 0.9839 - loss: 0.0400 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 302ms/step - accuracy: 0.9839 - loss: 0.0400 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 302ms/step - accuracy: 0.9839 - loss: 0.0400 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 302ms/step - accuracy: 0.9839 - loss: 0.0400 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 302ms/step - accuracy: 0.9839 - loss: 0.0399 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 302ms/step - accuracy: 0.9839 - loss: 0.0399 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 302ms/step - accuracy: 0.9839 - loss: 0.0399 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 302ms/step - accuracy: 0.9839 - loss: 0.0399 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 302ms/step - accuracy: 0.9839 - loss: 0.0398 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 302ms/step - accuracy: 0.9839 - loss: 0.0398 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 302ms/step - accuracy: 0.9839 - loss: 0.0398 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 302ms/step - accuracy: 0.9839 - loss: 0.0398 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 302ms/step - accuracy: 0.9840 - loss: 0.0398 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 302ms/step - accuracy: 0.9840 - loss: 0.0397 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 302ms/step - accuracy: 0.9840 - loss: 0.0397 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 302ms/step - accuracy: 0.9840 - loss: 0.0397 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 302ms/step - accuracy: 0.9840 - loss: 0.0397 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 0.9840 - loss: 0.0397 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 0.9840 - loss: 0.0397 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 0.9840 - loss: 0.0396 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 0.9840 - loss: 0.0396 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 0.9840 - loss: 0.0396 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 0.9840 - loss: 0.0396 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 0.9840 - loss: 0.0396 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9840 - loss: 0.0396 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9840 - loss: 0.0395 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9840 - loss: 0.0395 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9840 - loss: 0.0395 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 0.9841 - loss: 0.0395 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 0.9841 - loss: 0.0395 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 0.9841 - loss: 0.0395 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 0.9841 - loss: 0.0394 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 0.9841 - loss: 0.0394 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 0.9841 - loss: 0.0394 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9841 - loss: 0.0394 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9841 - loss: 0.0394 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9841 - loss: 0.0394 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9841 - loss: 0.0393 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 0.9841 - loss: 0.0393 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 0.9841 - loss: 0.0393 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 0.9841 - loss: 0.0393 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 0.9841 - loss: 0.0393 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 0.9841 - loss: 0.0393 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 0.9841 - loss: 0.0393 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9841 - loss: 0.0392 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9841 - loss: 0.0392 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9842 - loss: 0.0392 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9842 - loss: 0.0392 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 0.9842 - loss: 0.0392 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 0.9842 - loss: 0.0392 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 0.9842 - loss: 0.0392 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 0.9842 - loss: 0.0391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 0.9842 - loss: 0.0391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 0.9842 - loss: 0.0391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9842 - loss: 0.0391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9842 - loss: 0.0391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9842 - loss: 0.0391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9842 - loss: 0.0391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877 120/120 ━━━━━━━━━━━━━━━━━━━━ 37s 305ms/step - accuracy: 0.9848 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 - val_accuracy: 0.9764 - val_loss: 0.0732 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4888 Epoch 10/10  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9827 - loss: 0.0410 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9822 - loss: 0.0419 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9822 - loss: 0.0417 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9826 - loss: 0.0408 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4879  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9830 - loss: 0.0401 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9832 - loss: 0.0396 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 0.9834 - loss: 0.0392 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4883  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9836 - loss: 0.0388 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4884  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9838 - loss: 0.0383 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4885  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9840 - loss: 0.0380 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4885  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9841 - loss: 0.0378 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4885  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9842 - loss: 0.0377 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9843 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9843 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9843 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9844 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9844 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9844 - loss: 0.0372 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9844 - loss: 0.0372 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9845 - loss: 0.0371 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9845 - loss: 0.0370 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9845 - loss: 0.0370 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9846 - loss: 0.0369 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9846 - loss: 0.0369 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9846 - loss: 0.0368 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9846 - loss: 0.0368 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9846 - loss: 0.0368 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9846 - loss: 0.0368 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9847 - loss: 0.0367 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9847 - loss: 0.0367 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9847 - loss: 0.0367 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9847 - loss: 0.0367 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9847 - loss: 0.0366 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9847 - loss: 0.0366 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9848 - loss: 0.0366 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9848 - loss: 0.0365 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9848 - loss: 0.0365 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9848 - loss: 0.0365 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9848 - loss: 0.0365 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9848 - loss: 0.0365 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9848 - loss: 0.0365 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9848 - loss: 0.0364 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9848 - loss: 0.0364 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9848 - loss: 0.0364 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9849 - loss: 0.0364 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9849 - loss: 0.0364 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9849 - loss: 0.0363 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9849 - loss: 0.0363 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9849 - loss: 0.0363 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9849 - loss: 0.0363 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9849 - loss: 0.0363 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9849 - loss: 0.0363 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9849 - loss: 0.0362 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9849 - loss: 0.0362 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9849 - loss: 0.0362 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9849 - loss: 0.0362 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9850 - loss: 0.0362 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9850 - loss: 0.0362 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9850 - loss: 0.0361 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9850 - loss: 0.0361 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9850 - loss: 0.0361 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9850 - loss: 0.0361 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9850 - loss: 0.0361 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9850 - loss: 0.0361 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9850 - loss: 0.0361 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9850 - loss: 0.0361 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9850 - loss: 0.0360 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9850 - loss: 0.0360 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9850 - loss: 0.0360 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9850 - loss: 0.0360 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9850 - loss: 0.0360 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9851 - loss: 0.0360 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9851 - loss: 0.0359 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9851 - loss: 0.0359 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9851 - loss: 0.0359 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9851 - loss: 0.0359 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9851 - loss: 0.0359 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9851 - loss: 0.0359 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9851 - loss: 0.0359 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9851 - loss: 0.0358 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9851 - loss: 0.0358 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9851 - loss: 0.0358 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9851 - loss: 0.0358 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9851 - loss: 0.0358 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9851 - loss: 0.0358 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9851 - loss: 0.0358 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9851 - loss: 0.0357 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9852 - loss: 0.0357 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9852 - loss: 0.0357 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9852 - loss: 0.0357 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9852 - loss: 0.0357 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9852 - loss: 0.0357 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9852 - loss: 0.0357 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9852 - loss: 0.0356 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9852 - loss: 0.0356 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9852 - loss: 0.0356 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9852 - loss: 0.0356 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9852 - loss: 0.0356 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9852 - loss: 0.0356 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9852 - loss: 0.0356 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9852 - loss: 0.0356 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9852 - loss: 0.0355 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9852 - loss: 0.0355 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9852 - loss: 0.0355 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9852 - loss: 0.0355 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9852 - loss: 0.0355 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9853 - loss: 0.0355 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9853 - loss: 0.0355 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9853 - loss: 0.0355 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9853 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9853 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9853 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9853 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9853 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9853 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9853 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9853 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9853 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9853 - loss: 0.0353 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9853 - loss: 0.0353 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889 120/120 ━━━━━━━━━━━━━━━━━━━━ 37s 305ms/step - accuracy: 0.9859 - loss: 0.0339 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4892 - val_accuracy: 0.9772 - val_loss: 0.0733 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4902 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 510ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 519ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 43ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 52ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 52ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 52ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step 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