Files
segmentation-outputs/0-10.txt
2026-03-18 20:25:50 +01:00

435 lines
488 KiB
Plaintext
Raw Blame History

This file contains invisible Unicode characters

This file contains invisible Unicode characters that are indistinguishable to humans but may be processed differently by a computer. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

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 - mean_squared_error: 0.3418
 37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.8223 - loss: 0.4396 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3427
 38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.8247 - loss: 0.4357 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3436
 39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.8270 - loss: 0.4319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3446
 40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.8292 - loss: 0.4282 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3455
 41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.8313 - loss: 0.4247 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3464
 42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.8334 - loss: 0.4212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3473
 43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.8353 - loss: 0.4178 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3482
 44/120 ━━━━━━━━━━━━━━━━━━━━ 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 - accuracy: 0.8491 - loss: 0.3938 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3548
 52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.8506 - loss: 0.3912 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3556
 53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.8520 - loss: 0.3886 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3564
 54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.8534 - loss: 0.3860 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3572
 55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.8548 - loss: 0.3835 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3579
 56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.8561 - loss: 0.3811 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3587
 57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.8574 - loss: 0.3787 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3594
 58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.8587 - loss: 0.3764 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3601
 59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.8599 - loss: 0.3741 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3608
 60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.8611 - loss: 0.3719 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3616
 61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.8623 - loss: 0.3697 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3622
 62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.8634 - loss: 0.3676 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3629
 63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.8645 - loss: 0.3655 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3636
 64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.8656 - loss: 0.3634 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3643
 65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.8666 - loss: 0.3614 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3649
 66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.8677 - loss: 0.3594 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3656
 67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.8687 - loss: 0.3575 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3662
 68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.8697 - loss: 0.3556 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3668
 69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.8706 - loss: 0.3537 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3675
 70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.8716 - loss: 0.3519 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3681
 71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.8725 - loss: 0.3501 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3687
 72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.8734 - loss: 0.3483 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3693
 73/120 ━━━━━━━━━━━━━━━━━━━━ 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 - accuracy: 0.8800 - loss: 0.3352 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3737
 81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.8807 - loss: 0.3336 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3743
 82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.8815 - loss: 0.3321 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3748
 83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.8822 - loss: 0.3307 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3753
 84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.8829 - loss: 0.3292 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3758
 85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.8836 - loss: 0.3278 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3763
 86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.8843 - loss: 0.3264 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3768
 87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.8850 - loss: 0.3250 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3773 
 88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.8856 - loss: 0.3236 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3777
 89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.8863 - loss: 0.3223 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3782
 90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.8869 - loss: 0.3209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3787
 91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.8875 - loss: 0.3196 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3791
 92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.8882 - loss: 0.3183 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3796
 93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.8888 - loss: 0.3170 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3801
 94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.8894 - loss: 0.3158 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3805
 95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.8900 - loss: 0.3145 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3809
 96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.8905 - loss: 0.3133 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3814
 97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.8911 - loss: 0.3121 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3818
 98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.8917 - loss: 0.3109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3822
 99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.8922 - loss: 0.3097 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3826
100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.8928 - loss: 0.3086 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3831
101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.8933 - loss: 0.3074 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3835
102/120 ━━━━━━━━━━━━━━━━━━━━ 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 - accuracy: 0.9815 - loss: 0.0494 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4850
 26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9815 - loss: 0.0494 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4851
 27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9815 - loss: 0.0493 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4851
 28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9816 - loss: 0.0493 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4851
 29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9816 - loss: 0.0492 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4851
 30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9816 - loss: 0.0491 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4851
 31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 0.9816 - loss: 0.0491 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4851
 32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 0.9816 - loss: 0.0490 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4851
 33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 0.9817 - loss: 0.0490 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4851
 34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 0.9817 - loss: 0.0489 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4851
 35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 0.9817 - loss: 0.0488 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4852
 36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 0.9817 - loss: 0.0488 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4852
 37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 0.9817 - loss: 0.0487 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4852
 38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 0.9817 - loss: 0.0487 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4852
 39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 0.9818 - loss: 0.0487 - mean_absolute_error: 0.5000 - 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
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 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 52ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 52ms/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 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 63ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/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
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/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 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/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
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 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/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 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 57ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 60ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 69ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/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 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 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/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
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 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 58ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/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
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/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
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/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 45ms/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 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/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 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/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 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/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 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/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 43ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 52ms/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
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 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 43ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 52ms/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 43ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 52ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 52ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/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
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/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 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 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/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
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step