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b/examples/vision/ipynb/masked_image_modeling.ipynb index de21a9829b..e0b42452a3 100644 --- a/examples/vision/ipynb/masked_image_modeling.ipynb +++ b/examples/vision/ipynb/masked_image_modeling.ipynb @@ -59,14 +59,7 @@ "colab_type": "text" }, "source": [ - "## Imports\n", - "\n", - "This example requires TensorFlow Addons, which can be installed using the following\n", - "command:\n", - "\n", - "```shell\n", - "pip install -U tensorflow-addons\n", - "```" + "## Imports" ] }, { @@ -77,10 +70,13 @@ }, "outputs": [], "source": [ - "from tensorflow.keras import layers\n", - "import tensorflow_addons as tfa\n", - "from tensorflow import keras\n", + "import os\n", + "\n", + "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n", + "\n", "import tensorflow as tf\n", + "import keras\n", + "from keras import layers\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -233,7 +229,10 @@ "\n", "def get_test_augmentation_model():\n", " model = keras.Sequential(\n", - " [layers.Rescaling(1 / 255.0), layers.Resizing(IMAGE_SIZE, IMAGE_SIZE),],\n", + " [\n", + " layers.Rescaling(1 / 255.0),\n", + " layers.Resizing(IMAGE_SIZE, IMAGE_SIZE),\n", + " ],\n", " name=\"test_data_augmentation\",\n", " )\n", " return model\n", @@ -775,7 +774,7 @@ " loss_output = tf.gather(decoder_patches, mask_indices, axis=1, batch_dims=1)\n", "\n", " # Compute the total loss.\n", - " total_loss = self.compiled_loss(loss_patch, loss_output)\n", + " total_loss = self.compute_loss(y=loss_patch, y_pred=loss_output)\n", "\n", " return total_loss, loss_patch, loss_output\n", "\n", @@ -793,21 +792,27 @@ " ]\n", " grads = tape.gradient(total_loss, train_vars)\n", " tv_list = []\n", - " for (grad, var) in zip(grads, train_vars):\n", + " for grad, var in zip(grads, train_vars):\n", " for g, v in zip(grad, var):\n", " tv_list.append((g, v))\n", " self.optimizer.apply_gradients(tv_list)\n", "\n", " # Report progress.\n", - " self.compiled_metrics.update_state(loss_patch, loss_output)\n", - " return {m.name: m.result() for m in self.metrics}\n", + " results = {}\n", + " for metric in self.metrics:\n", + " metric.update_state(loss_patch, loss_output)\n", + " results[metric.name] = metric.result()\n", + " return results\n", "\n", " def test_step(self, images):\n", " total_loss, loss_patch, loss_output = self.calculate_loss(images, test=True)\n", "\n", " # Update the trackers.\n", - " self.compiled_metrics.update_state(loss_patch, loss_output)\n", - " return {m.name: m.result() for m in self.metrics}\n", + " results = {}\n", + " for metric in self.metrics:\n", + " metric.update_state(loss_patch, loss_output)\n", + " results[metric.name] = metric.result()\n", + " return results\n", "" ] }, @@ -1022,14 +1027,19 @@ }, "outputs": [], "source": [ - "optimizer = tfa.optimizers.AdamW(learning_rate=scheduled_lrs, weight_decay=WEIGHT_DECAY)\n", + "optimizer = keras.optimizers.AdamW(\n", + " learning_rate=scheduled_lrs, weight_decay=WEIGHT_DECAY\n", + ")\n", "\n", "# Compile and pretrain the model.\n", "mae_model.compile(\n", " optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=[\"mae\"]\n", ")\n", "history = mae_model.fit(\n", - " train_ds, epochs=EPOCHS, validation_data=val_ds, callbacks=train_callbacks,\n", + " train_ds,\n", + " epochs=EPOCHS,\n", + " validation_data=val_ds,\n", + " callbacks=train_callbacks,\n", ")\n", "\n", "# Measure its performance.\n", diff --git a/examples/vision/masked_image_modeling.py b/examples/vision/masked_image_modeling.py index 21703a7b49..afe850ae9b 100644 --- a/examples/vision/masked_image_modeling.py +++ b/examples/vision/masked_image_modeling.py @@ -43,19 +43,14 @@ """ ## Imports - -This example requires TensorFlow Addons, which can be installed using the following -command: - -```shell -pip install -U tensorflow-addons -``` """ +import os + +os.environ["KERAS_BACKEND"] = "tensorflow" -from tensorflow.keras import layers -import tensorflow_addons as tfa -from tensorflow import keras import tensorflow as tf +import keras +from keras import layers import matplotlib.pyplot as plt import numpy as np @@ -599,7 +594,7 @@ def calculate_loss(self, images, test=False): loss_output = tf.gather(decoder_patches, mask_indices, axis=1, batch_dims=1) # Compute the total loss. - total_loss = self.compiled_loss(loss_patch, loss_output) + total_loss = self.compute_loss(y=loss_patch, y_pred=loss_output) return total_loss, loss_patch, loss_output @@ -623,15 +618,21 @@ def train_step(self, images): self.optimizer.apply_gradients(tv_list) # Report progress. - self.compiled_metrics.update_state(loss_patch, loss_output) - return {m.name: m.result() for m in self.metrics} + results = {} + for metric in self.metrics: + metric.update_state(loss_patch, loss_output) + results[metric.name] = metric.result() + return results def test_step(self, images): total_loss, loss_patch, loss_output = self.calculate_loss(images, test=True) # Update the trackers. - self.compiled_metrics.update_state(loss_patch, loss_output) - return {m.name: m.result() for m in self.metrics} + results = {} + for metric in self.metrics: + metric.update_state(loss_patch, loss_output) + results[metric.name] = metric.result() + return results """ @@ -785,7 +786,9 @@ def __call__(self, step): ## Model compilation and training """ -optimizer = tfa.optimizers.AdamW(learning_rate=scheduled_lrs, weight_decay=WEIGHT_DECAY) +optimizer = keras.optimizers.AdamW( + learning_rate=scheduled_lrs, weight_decay=WEIGHT_DECAY +) # Compile and pretrain the model. mae_model.compile( diff --git a/examples/vision/md/masked_image_modeling.md b/examples/vision/md/masked_image_modeling.md index 7e6c92baf6..30b9638670 100644 --- a/examples/vision/md/masked_image_modeling.md +++ b/examples/vision/md/masked_image_modeling.md @@ -46,19 +46,15 @@ As a reference, we reuse some of the code presented in --- ## Imports -This example requires TensorFlow Addons, which can be installed using the following -command: -```shell -pip install -U tensorflow-addons -``` +```python +import os +os.environ["KERAS_BACKEND"] = "tensorflow" -```python -from tensorflow.keras import layers -import tensorflow_addons as tfa -from tensorflow import keras import tensorflow as tf +import keras +from keras import layers import matplotlib.pyplot as plt import numpy as np @@ -149,10 +145,6 @@ Training samples: 40000 Validation samples: 10000 Testing samples: 10000 -2021-11-24 01:10:52.088318: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA -To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. -2021-11-24 01:10:54.356762: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 38444 MB memory: -> device: 0, name: A100-SXM4-40GB, pci bus id: 0000:00:04.0, compute capability: 8.0 - ``` --- @@ -187,7 +179,10 @@ def get_train_augmentation_model(): def get_test_augmentation_model(): model = keras.Sequential( - [layers.Rescaling(1 / 255.0), layers.Resizing(IMAGE_SIZE, IMAGE_SIZE),], + [ + layers.Rescaling(1 / 255.0), + layers.Resizing(IMAGE_SIZE, IMAGE_SIZE), + ], name="test_data_augmentation", ) return model @@ -490,14 +485,9 @@ plt.title("Original") plt.show() ``` -
-``` -2021-11-24 01:11:00.182447: I tensorflow/stream_executor/cuda/cuda_blas.cc:1774] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once. -``` -
-![png](/img/examples/vision/masked_image_modeling/masked_image_modeling_17_1.png) +![png](/img/examples/vision/masked_image_modeling/masked_image_modeling_17_0.png) @@ -665,7 +655,7 @@ class MaskedAutoencoder(keras.Model): loss_output = tf.gather(decoder_patches, mask_indices, axis=1, batch_dims=1) # Compute the total loss. - total_loss = self.compiled_loss(loss_patch, loss_output) + total_loss = self.compute_loss(y=loss_patch, y_pred=loss_output) return total_loss, loss_patch, loss_output @@ -683,21 +673,27 @@ class MaskedAutoencoder(keras.Model): ] grads = tape.gradient(total_loss, train_vars) tv_list = [] - for (grad, var) in zip(grads, train_vars): + for grad, var in zip(grads, train_vars): for g, v in zip(grad, var): tv_list.append((g, v)) self.optimizer.apply_gradients(tv_list) # Report progress. - self.compiled_metrics.update_state(loss_patch, loss_output) - return {m.name: m.result() for m in self.metrics} + results = {} + for metric in self.metrics: + metric.update_state(loss_patch, loss_output) + results[metric.name] = metric.result() + return results def test_step(self, images): total_loss, loss_patch, loss_output = self.calculate_loss(images, test=True) # Update the trackers. - self.compiled_metrics.update_state(loss_patch, loss_output) - return {m.name: m.result() for m in self.metrics} + results = {} + for metric in self.metrics: + metric.update_state(loss_patch, loss_output) + results[metric.name] = metric.result() + return results ``` @@ -862,14 +858,19 @@ train_callbacks = [TrainMonitor(epoch_interval=5)] ```python -optimizer = tfa.optimizers.AdamW(learning_rate=scheduled_lrs, weight_decay=WEIGHT_DECAY) +optimizer = keras.optimizers.AdamW( + learning_rate=scheduled_lrs, weight_decay=WEIGHT_DECAY +) # Compile and pretrain the model. mae_model.compile( optimizer=optimizer, loss=keras.losses.MeanSquaredError(), metrics=["mae"] ) history = mae_model.fit( - train_ds, epochs=EPOCHS, validation_data=val_ds, callbacks=train_callbacks, + train_ds, + epochs=EPOCHS, + validation_data=val_ds, + callbacks=train_callbacks, ) # Measure its performance. @@ -881,7 +882,7 @@ print(f"MAE: {mae:.2f}")
``` Epoch 1/100 -157/157 [==============================] - ETA: 0s - loss: 0.0507 - mae: 0.1811 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 80ms/step - mae: 0.2035 - loss: 0.4828 Idx chosen: 92 ``` @@ -893,17 +894,17 @@ Idx chosen: 92
``` -157/157 [==============================] - 19s 54ms/step - loss: 0.0507 - mae: 0.1811 - val_loss: 0.0417 - val_mae: 0.1630 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 47s 95ms/step - mae: 0.2033 - loss: 0.4828 - val_loss: 0.5225 - val_mae: 0.1600 Epoch 2/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0385 - mae: 0.1550 - val_loss: 0.0349 - val_mae: 0.1460 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 83ms/step - mae: 0.1592 - loss: 0.5128 - val_loss: 0.5290 - val_mae: 0.1511 Epoch 3/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0336 - mae: 0.1420 - val_loss: 0.0311 - val_mae: 0.1352 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1530 - loss: 0.5193 - val_loss: 0.5336 - val_mae: 0.1478 Epoch 4/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0299 - mae: 0.1325 - val_loss: 0.0302 - val_mae: 0.1321 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1502 - loss: 0.5220 - val_loss: 0.5298 - val_mae: 0.1436 Epoch 5/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0269 - mae: 0.1246 - val_loss: 0.0256 - val_mae: 0.1207 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1458 - loss: 0.5245 - val_loss: 0.5296 - val_mae: 0.1405 Epoch 6/100 -156/157 [============================>.] - ETA: 0s - loss: 0.0246 - mae: 0.1181 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 81ms/step - mae: 0.1414 - loss: 0.5265 Idx chosen: 14 ``` @@ -915,17 +916,17 @@ Idx chosen: 14
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0246 - mae: 0.1181 - val_loss: 0.0241 - val_mae: 0.1166 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 14s 88ms/step - mae: 0.1414 - loss: 0.5265 - val_loss: 0.5328 - val_mae: 0.1402 Epoch 7/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0232 - mae: 0.1142 - val_loss: 0.0237 - val_mae: 0.1152 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1399 - loss: 0.5278 - val_loss: 0.5361 - val_mae: 0.1360 Epoch 8/100 -157/157 [==============================] - 7s 43ms/step - loss: 0.0222 - mae: 0.1113 - val_loss: 0.0216 - val_mae: 0.1088 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1389 - loss: 0.5285 - val_loss: 0.5365 - val_mae: 0.1424 Epoch 9/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0214 - mae: 0.1086 - val_loss: 0.0217 - val_mae: 0.1096 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1379 - loss: 0.5295 - val_loss: 0.5312 - val_mae: 0.1345 Epoch 10/100 -157/157 [==============================] - 7s 43ms/step - loss: 0.0206 - mae: 0.1064 - val_loss: 0.0215 - val_mae: 0.1100 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1352 - loss: 0.5308 - val_loss: 0.5374 - val_mae: 0.1321 Epoch 11/100 -157/157 [==============================] - ETA: 0s - loss: 0.0203 - mae: 0.1053 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 81ms/step - mae: 0.1339 - loss: 0.5317 Idx chosen: 106 ``` @@ -937,17 +938,17 @@ Idx chosen: 106
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0203 - mae: 0.1053 - val_loss: 0.0205 - val_mae: 0.1052 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 14s 87ms/step - mae: 0.1339 - loss: 0.5317 - val_loss: 0.5392 - val_mae: 0.1330 Epoch 12/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0200 - mae: 0.1043 - val_loss: 0.0196 - val_mae: 0.1028 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1321 - loss: 0.5331 - val_loss: 0.5383 - val_mae: 0.1301 Epoch 13/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0196 - mae: 0.1030 - val_loss: 0.0198 - val_mae: 0.1043 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1317 - loss: 0.5343 - val_loss: 0.5405 - val_mae: 0.1322 Epoch 14/100 -157/157 [==============================] - 7s 43ms/step - loss: 0.0193 - mae: 0.1019 - val_loss: 0.0192 - val_mae: 0.1004 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1326 - loss: 0.5338 - val_loss: 0.5404 - val_mae: 0.1280 Epoch 15/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0191 - mae: 0.1013 - val_loss: 0.0198 - val_mae: 0.1031 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 84ms/step - mae: 0.1297 - loss: 0.5343 - val_loss: 0.5444 - val_mae: 0.1261 Epoch 16/100 -157/157 [==============================] - ETA: 0s - loss: 0.0189 - mae: 0.1007 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 82ms/step - mae: 0.1276 - loss: 0.5361 Idx chosen: 71 ``` @@ -959,17 +960,17 @@ Idx chosen: 71
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0189 - mae: 0.1007 - val_loss: 0.0188 - val_mae: 0.1003 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 14s 91ms/step - mae: 0.1276 - loss: 0.5362 - val_loss: 0.5456 - val_mae: 0.1243 Epoch 17/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0185 - mae: 0.0992 - val_loss: 0.0187 - val_mae: 0.0993 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 83ms/step - mae: 0.1262 - loss: 0.5382 - val_loss: 0.5427 - val_mae: 0.1233 Epoch 18/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0185 - mae: 0.0992 - val_loss: 0.0192 - val_mae: 0.1021 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1221 - loss: 0.5407 - val_loss: 0.5473 - val_mae: 0.1196 Epoch 19/100 -157/157 [==============================] - 7s 43ms/step - loss: 0.0182 - mae: 0.0984 - val_loss: 0.0181 - val_mae: 0.0967 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1209 - loss: 0.5412 - val_loss: 0.5511 - val_mae: 0.1176 Epoch 20/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0180 - mae: 0.0975 - val_loss: 0.0183 - val_mae: 0.0996 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 83ms/step - mae: 0.1202 - loss: 0.5422 - val_loss: 0.5515 - val_mae: 0.1167 Epoch 21/100 -156/157 [============================>.] - ETA: 0s - loss: 0.0180 - mae: 0.0975 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 79ms/step - mae: 0.1186 - loss: 0.5430 Idx chosen: 188 ``` @@ -981,17 +982,17 @@ Idx chosen: 188
``` -157/157 [==============================] - 7s 47ms/step - loss: 0.0180 - mae: 0.0975 - val_loss: 0.0185 - val_mae: 0.0992 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 85ms/step - mae: 0.1186 - loss: 0.5430 - val_loss: 0.5546 - val_mae: 0.1168 Epoch 22/100 -157/157 [==============================] - 7s 45ms/step - loss: 0.0179 - mae: 0.0971 - val_loss: 0.0181 - val_mae: 0.0977 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1171 - loss: 0.5446 - val_loss: 0.5500 - val_mae: 0.1155 Epoch 23/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0178 - mae: 0.0966 - val_loss: 0.0179 - val_mae: 0.0962 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1161 - loss: 0.5457 - val_loss: 0.5559 - val_mae: 0.1135 Epoch 24/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0178 - mae: 0.0966 - val_loss: 0.0176 - val_mae: 0.0952 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 83ms/step - mae: 0.1135 - loss: 0.5479 - val_loss: 0.5521 - val_mae: 0.1112 Epoch 25/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0176 - mae: 0.0960 - val_loss: 0.0182 - val_mae: 0.0984 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1128 - loss: 0.5480 - val_loss: 0.5505 - val_mae: 0.1122 Epoch 26/100 -157/157 [==============================] - ETA: 0s - loss: 0.0175 - mae: 0.0958 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 79ms/step - mae: 0.1123 - loss: 0.5470 Idx chosen: 20 ``` @@ -1003,17 +1004,17 @@ Idx chosen: 20
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0175 - mae: 0.0958 - val_loss: 0.0176 - val_mae: 0.0958 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 86ms/step - mae: 0.1123 - loss: 0.5470 - val_loss: 0.5572 - val_mae: 0.1127 Epoch 27/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0175 - mae: 0.0957 - val_loss: 0.0175 - val_mae: 0.0948 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1114 - loss: 0.5487 - val_loss: 0.5555 - val_mae: 0.1092 Epoch 28/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0175 - mae: 0.0956 - val_loss: 0.0173 - val_mae: 0.0947 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1108 - loss: 0.5492 - val_loss: 0.5569 - val_mae: 0.1110 Epoch 29/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0172 - mae: 0.0949 - val_loss: 0.0174 - val_mae: 0.0948 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 83ms/step - mae: 0.1104 - loss: 0.5491 - val_loss: 0.5517 - val_mae: 0.1110 Epoch 30/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0172 - mae: 0.0948 - val_loss: 0.0174 - val_mae: 0.0944 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1099 - loss: 0.5490 - val_loss: 0.5543 - val_mae: 0.1104 Epoch 31/100 -157/157 [==============================] - ETA: 0s - loss: 0.0172 - mae: 0.0945 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 79ms/step - mae: 0.1095 - loss: 0.5501 Idx chosen: 102 ``` @@ -1025,17 +1026,17 @@ Idx chosen: 102
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0172 - mae: 0.0945 - val_loss: 0.0169 - val_mae: 0.0932 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 86ms/step - mae: 0.1095 - loss: 0.5501 - val_loss: 0.5578 - val_mae: 0.1108 Epoch 32/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0172 - mae: 0.0947 - val_loss: 0.0174 - val_mae: 0.0961 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1089 - loss: 0.5503 - val_loss: 0.5620 - val_mae: 0.1081 Epoch 33/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0171 - mae: 0.0945 - val_loss: 0.0171 - val_mae: 0.0937 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1079 - loss: 0.5509 - val_loss: 0.5618 - val_mae: 0.1067 Epoch 34/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0170 - mae: 0.0938 - val_loss: 0.0171 - val_mae: 0.0941 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 83ms/step - mae: 0.1067 - loss: 0.5524 - val_loss: 0.5627 - val_mae: 0.1059 Epoch 35/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0170 - mae: 0.0940 - val_loss: 0.0171 - val_mae: 0.0948 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1068 - loss: 0.5515 - val_loss: 0.5576 - val_mae: 0.1050 Epoch 36/100 -157/157 [==============================] - ETA: 0s - loss: 0.0168 - mae: 0.0933 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 79ms/step - mae: 0.1057 - loss: 0.5526 Idx chosen: 121 ``` @@ -1047,17 +1048,17 @@ Idx chosen: 121
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0168 - mae: 0.0933 - val_loss: 0.0170 - val_mae: 0.0935 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 86ms/step - mae: 0.1057 - loss: 0.5526 - val_loss: 0.5627 - val_mae: 0.1050 Epoch 37/100 -157/157 [==============================] - 7s 43ms/step - loss: 0.0169 - mae: 0.0935 - val_loss: 0.0168 - val_mae: 0.0933 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1065 - loss: 0.5534 - val_loss: 0.5638 - val_mae: 0.1050 Epoch 38/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0168 - mae: 0.0933 - val_loss: 0.0170 - val_mae: 0.0935 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 83ms/step - mae: 0.1055 - loss: 0.5528 - val_loss: 0.5527 - val_mae: 0.1083 Epoch 39/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0167 - mae: 0.0931 - val_loss: 0.0169 - val_mae: 0.0934 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 20s 82ms/step - mae: 0.1056 - loss: 0.5516 - val_loss: 0.5562 - val_mae: 0.1044 Epoch 40/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0167 - mae: 0.0930 - val_loss: 0.0169 - val_mae: 0.0934 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1053 - loss: 0.5528 - val_loss: 0.5567 - val_mae: 0.1051 Epoch 41/100 -157/157 [==============================] - ETA: 0s - loss: 0.0167 - mae: 0.0929 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 78ms/step - mae: 0.1049 - loss: 0.5533 Idx chosen: 210 ``` @@ -1069,17 +1070,17 @@ Idx chosen: 210
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0167 - mae: 0.0929 - val_loss: 0.0169 - val_mae: 0.0930 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 85ms/step - mae: 0.1049 - loss: 0.5533 - val_loss: 0.5620 - val_mae: 0.1030 Epoch 42/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0167 - mae: 0.0928 - val_loss: 0.0170 - val_mae: 0.0941 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 83ms/step - mae: 0.1041 - loss: 0.5534 - val_loss: 0.5650 - val_mae: 0.1052 Epoch 43/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0166 - mae: 0.0925 - val_loss: 0.0169 - val_mae: 0.0931 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1048 - loss: 0.5526 - val_loss: 0.5619 - val_mae: 0.1027 Epoch 44/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0165 - mae: 0.0921 - val_loss: 0.0165 - val_mae: 0.0914 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1037 - loss: 0.5543 - val_loss: 0.5615 - val_mae: 0.1031 Epoch 45/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0165 - mae: 0.0922 - val_loss: 0.0165 - val_mae: 0.0915 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1036 - loss: 0.5535 - val_loss: 0.5575 - val_mae: 0.1026 Epoch 46/100 -157/157 [==============================] - ETA: 0s - loss: 0.0165 - mae: 0.0922 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 78ms/step - mae: 0.1032 - loss: 0.5537 Idx chosen: 214 ``` @@ -1091,17 +1092,17 @@ Idx chosen: 214
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0165 - mae: 0.0922 - val_loss: 0.0166 - val_mae: 0.0914 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 85ms/step - mae: 0.1032 - loss: 0.5537 - val_loss: 0.5549 - val_mae: 0.1037 Epoch 47/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0164 - mae: 0.0919 - val_loss: 0.0164 - val_mae: 0.0912 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 84ms/step - mae: 0.1035 - loss: 0.5539 - val_loss: 0.5597 - val_mae: 0.1031 Epoch 48/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0163 - mae: 0.0914 - val_loss: 0.0166 - val_mae: 0.0923 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1033 - loss: 0.5533 - val_loss: 0.5650 - val_mae: 0.1013 Epoch 49/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0163 - mae: 0.0914 - val_loss: 0.0164 - val_mae: 0.0914 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.1027 - loss: 0.5543 - val_loss: 0.5571 - val_mae: 0.1028 Epoch 50/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0162 - mae: 0.0912 - val_loss: 0.0164 - val_mae: 0.0916 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1024 - loss: 0.5548 - val_loss: 0.5592 - val_mae: 0.1018 Epoch 51/100 -157/157 [==============================] - ETA: 0s - loss: 0.0162 - mae: 0.0913 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 78ms/step - mae: 0.1025 - loss: 0.5543 Idx chosen: 74 ``` @@ -1113,17 +1114,17 @@ Idx chosen: 74
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0162 - mae: 0.0913 - val_loss: 0.0165 - val_mae: 0.0919 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 85ms/step - mae: 0.1025 - loss: 0.5543 - val_loss: 0.5645 - val_mae: 0.1007 Epoch 52/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0162 - mae: 0.0909 - val_loss: 0.0163 - val_mae: 0.0912 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 83ms/step - mae: 0.1025 - loss: 0.5544 - val_loss: 0.5616 - val_mae: 0.1004 Epoch 53/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0161 - mae: 0.0908 - val_loss: 0.0161 - val_mae: 0.0903 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1014 - loss: 0.5547 - val_loss: 0.5594 - val_mae: 0.1007 Epoch 54/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0161 - mae: 0.0908 - val_loss: 0.0162 - val_mae: 0.0901 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1014 - loss: 0.5550 - val_loss: 0.5687 - val_mae: 0.1012 Epoch 55/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0161 - mae: 0.0907 - val_loss: 0.0162 - val_mae: 0.0909 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1022 - loss: 0.5551 - val_loss: 0.5572 - val_mae: 0.1018 Epoch 56/100 -156/157 [============================>.] - ETA: 0s - loss: 0.0160 - mae: 0.0904 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 79ms/step - mae: 0.1015 - loss: 0.5558 Idx chosen: 202 ``` @@ -1135,17 +1136,17 @@ Idx chosen: 202
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0160 - mae: 0.0904 - val_loss: 0.0160 - val_mae: 0.0908 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 86ms/step - mae: 0.1015 - loss: 0.5558 - val_loss: 0.5619 - val_mae: 0.0996 Epoch 57/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0159 - mae: 0.0902 - val_loss: 0.0160 - val_mae: 0.0899 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1008 - loss: 0.5550 - val_loss: 0.5614 - val_mae: 0.0996 Epoch 58/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0159 - mae: 0.0901 - val_loss: 0.0162 - val_mae: 0.0916 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1004 - loss: 0.5557 - val_loss: 0.5620 - val_mae: 0.0995 Epoch 59/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0159 - mae: 0.0898 - val_loss: 0.0160 - val_mae: 0.0903 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.1002 - loss: 0.5558 - val_loss: 0.5612 - val_mae: 0.0997 Epoch 60/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0159 - mae: 0.0898 - val_loss: 0.0159 - val_mae: 0.0897 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.1005 - loss: 0.5563 - val_loss: 0.5598 - val_mae: 0.1000 Epoch 61/100 -157/157 [==============================] - ETA: 0s - loss: 0.0158 - mae: 0.0894 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 79ms/step - mae: 0.1001 - loss: 0.5564 Idx chosen: 87 ``` @@ -1157,17 +1158,17 @@ Idx chosen: 87
``` -157/157 [==============================] - 7s 48ms/step - loss: 0.0158 - mae: 0.0894 - val_loss: 0.0160 - val_mae: 0.0895 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 86ms/step - mae: 0.1001 - loss: 0.5564 - val_loss: 0.5606 - val_mae: 0.0998 Epoch 62/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0158 - mae: 0.0895 - val_loss: 0.0161 - val_mae: 0.0905 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 86ms/step - mae: 0.0998 - loss: 0.5562 - val_loss: 0.5643 - val_mae: 0.0988 Epoch 63/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0157 - mae: 0.0891 - val_loss: 0.0158 - val_mae: 0.0894 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.1001 - loss: 0.5556 - val_loss: 0.5657 - val_mae: 0.0985 Epoch 64/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0157 - mae: 0.0890 - val_loss: 0.0158 - val_mae: 0.0889 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.0998 - loss: 0.5566 - val_loss: 0.5624 - val_mae: 0.0989 Epoch 65/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0157 - mae: 0.0890 - val_loss: 0.0159 - val_mae: 0.0893 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.0994 - loss: 0.5564 - val_loss: 0.5576 - val_mae: 0.0999 Epoch 66/100 -157/157 [==============================] - ETA: 0s - loss: 0.0156 - mae: 0.0888 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 79ms/step - mae: 0.0993 - loss: 0.5567 Idx chosen: 116 ``` @@ -1179,17 +1180,17 @@ Idx chosen: 116
``` -157/157 [==============================] - 7s 47ms/step - loss: 0.0156 - mae: 0.0888 - val_loss: 0.0160 - val_mae: 0.0903 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 86ms/step - mae: 0.0993 - loss: 0.5567 - val_loss: 0.5572 - val_mae: 0.1000 Epoch 67/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0156 - mae: 0.0886 - val_loss: 0.0156 - val_mae: 0.0881 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.0990 - loss: 0.5570 - val_loss: 0.5619 - val_mae: 0.0981 Epoch 68/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0155 - mae: 0.0883 - val_loss: 0.0156 - val_mae: 0.0885 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.0987 - loss: 0.5578 - val_loss: 0.5644 - val_mae: 0.0973 Epoch 69/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0154 - mae: 0.0881 - val_loss: 0.0155 - val_mae: 0.0878 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.0981 - loss: 0.5577 - val_loss: 0.5639 - val_mae: 0.0976 Epoch 70/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0154 - mae: 0.0881 - val_loss: 0.0158 - val_mae: 0.0891 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.0986 - loss: 0.5563 - val_loss: 0.5601 - val_mae: 0.0989 Epoch 71/100 -156/157 [============================>.] - ETA: 0s - loss: 0.0154 - mae: 0.0879 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 77ms/step - mae: 0.0982 - loss: 0.5578 Idx chosen: 99 ``` @@ -1201,17 +1202,17 @@ Idx chosen: 99
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0154 - mae: 0.0879 - val_loss: 0.0155 - val_mae: 0.0884 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 84ms/step - mae: 0.0982 - loss: 0.5577 - val_loss: 0.5628 - val_mae: 0.0970 Epoch 72/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0153 - mae: 0.0877 - val_loss: 0.0154 - val_mae: 0.0878 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.0979 - loss: 0.5569 - val_loss: 0.5637 - val_mae: 0.0968 Epoch 73/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0153 - mae: 0.0876 - val_loss: 0.0155 - val_mae: 0.0879 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.0979 - loss: 0.5575 - val_loss: 0.5606 - val_mae: 0.0975 Epoch 74/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0152 - mae: 0.0874 - val_loss: 0.0153 - val_mae: 0.0876 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.0977 - loss: 0.5572 - val_loss: 0.5628 - val_mae: 0.0967 Epoch 75/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0152 - mae: 0.0872 - val_loss: 0.0153 - val_mae: 0.0872 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.0975 - loss: 0.5572 - val_loss: 0.5631 - val_mae: 0.0964 Epoch 76/100 -157/157 [==============================] - ETA: 0s - loss: 0.0151 - mae: 0.0870 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 77ms/step - mae: 0.0973 - loss: 0.5580 Idx chosen: 103 ``` @@ -1223,17 +1224,17 @@ Idx chosen: 103
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0151 - mae: 0.0870 - val_loss: 0.0153 - val_mae: 0.0873 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 83ms/step - mae: 0.0973 - loss: 0.5579 - val_loss: 0.5628 - val_mae: 0.0967 Epoch 77/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0151 - mae: 0.0869 - val_loss: 0.0152 - val_mae: 0.0872 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.0974 - loss: 0.5579 - val_loss: 0.5638 - val_mae: 0.0963 Epoch 78/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0151 - mae: 0.0867 - val_loss: 0.0152 - val_mae: 0.0869 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.0968 - loss: 0.5585 - val_loss: 0.5615 - val_mae: 0.0967 Epoch 79/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0151 - mae: 0.0867 - val_loss: 0.0151 - val_mae: 0.0863 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.0969 - loss: 0.5578 - val_loss: 0.5641 - val_mae: 0.0959 Epoch 80/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0150 - mae: 0.0865 - val_loss: 0.0150 - val_mae: 0.0860 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.0967 - loss: 0.5584 - val_loss: 0.5619 - val_mae: 0.0962 Epoch 81/100 -157/157 [==============================] - ETA: 0s - loss: 0.0150 - mae: 0.0865 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 77ms/step - mae: 0.0965 - loss: 0.5578 Idx chosen: 151 ``` @@ -1245,17 +1246,17 @@ Idx chosen: 151
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0150 - mae: 0.0865 - val_loss: 0.0151 - val_mae: 0.0862 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 83ms/step - mae: 0.0965 - loss: 0.5578 - val_loss: 0.5651 - val_mae: 0.0957 Epoch 82/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0149 - mae: 0.0861 - val_loss: 0.0151 - val_mae: 0.0859 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.0965 - loss: 0.5583 - val_loss: 0.5644 - val_mae: 0.0957 Epoch 83/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0149 - mae: 0.0861 - val_loss: 0.0149 - val_mae: 0.0857 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.0962 - loss: 0.5584 - val_loss: 0.5649 - val_mae: 0.0954 Epoch 84/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0149 - mae: 0.0860 - val_loss: 0.0151 - val_mae: 0.0865 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.0962 - loss: 0.5586 - val_loss: 0.5611 - val_mae: 0.0962 Epoch 85/100 -157/157 [==============================] - 7s 43ms/step - loss: 0.0148 - mae: 0.0858 - val_loss: 0.0150 - val_mae: 0.0856 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.0961 - loss: 0.5582 - val_loss: 0.5638 - val_mae: 0.0956 Epoch 86/100 -157/157 [==============================] - ETA: 0s - loss: 0.0148 - mae: 0.0856 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 77ms/step - mae: 0.0961 - loss: 0.5584 Idx chosen: 130 ``` @@ -1267,17 +1268,17 @@ Idx chosen: 130
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0148 - mae: 0.0856 - val_loss: 0.0149 - val_mae: 0.0855 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 83ms/step - mae: 0.0961 - loss: 0.5584 - val_loss: 0.5641 - val_mae: 0.0954 Epoch 87/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0148 - mae: 0.0855 - val_loss: 0.0148 - val_mae: 0.0851 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.0959 - loss: 0.5580 - val_loss: 0.5641 - val_mae: 0.0953 Epoch 88/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0148 - mae: 0.0856 - val_loss: 0.0149 - val_mae: 0.0855 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.0960 - loss: 0.5583 - val_loss: 0.5642 - val_mae: 0.0953 Epoch 89/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0147 - mae: 0.0853 - val_loss: 0.0148 - val_mae: 0.0852 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.0958 - loss: 0.5591 - val_loss: 0.5635 - val_mae: 0.0953 Epoch 90/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0147 - mae: 0.0853 - val_loss: 0.0148 - val_mae: 0.0850 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.0957 - loss: 0.5587 - val_loss: 0.5648 - val_mae: 0.0948 Epoch 91/100 -157/157 [==============================] - ETA: 0s - loss: 0.0147 - mae: 0.0852 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 77ms/step - mae: 0.0957 - loss: 0.5585 Idx chosen: 149 ``` @@ -1289,17 +1290,17 @@ Idx chosen: 149
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0147 - mae: 0.0852 - val_loss: 0.0148 - val_mae: 0.0851 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 84ms/step - mae: 0.0957 - loss: 0.5585 - val_loss: 0.5636 - val_mae: 0.0952 Epoch 92/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0146 - mae: 0.0851 - val_loss: 0.0147 - val_mae: 0.0849 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.0957 - loss: 0.5593 - val_loss: 0.5642 - val_mae: 0.0950 Epoch 93/100 -157/157 [==============================] - 7s 43ms/step - loss: 0.0147 - mae: 0.0853 - val_loss: 0.0147 - val_mae: 0.0849 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.0957 - loss: 0.5598 - val_loss: 0.5635 - val_mae: 0.0950 Epoch 94/100 -157/157 [==============================] - 7s 43ms/step - loss: 0.0147 - mae: 0.0852 - val_loss: 0.0148 - val_mae: 0.0850 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.0956 - loss: 0.5587 - val_loss: 0.5641 - val_mae: 0.0950 Epoch 95/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0147 - mae: 0.0852 - val_loss: 0.0148 - val_mae: 0.0853 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.0955 - loss: 0.5587 - val_loss: 0.5637 - val_mae: 0.0950 Epoch 96/100 -157/157 [==============================] - ETA: 0s - loss: 0.0147 - mae: 0.0853 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 77ms/step - mae: 0.0956 - loss: 0.5585 Idx chosen: 52 ``` @@ -1311,18 +1312,18 @@ Idx chosen: 52
``` -157/157 [==============================] - 7s 46ms/step - loss: 0.0147 - mae: 0.0853 - val_loss: 0.0148 - val_mae: 0.0853 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 14s 87ms/step - mae: 0.0956 - loss: 0.5585 - val_loss: 0.5643 - val_mae: 0.0950 Epoch 97/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0148 - mae: 0.0856 - val_loss: 0.0149 - val_mae: 0.0855 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 81ms/step - mae: 0.0956 - loss: 0.5587 - val_loss: 0.5642 - val_mae: 0.0950 Epoch 98/100 -157/157 [==============================] - 7s 43ms/step - loss: 0.0148 - mae: 0.0857 - val_loss: 0.0149 - val_mae: 0.0858 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 82ms/step - mae: 0.0954 - loss: 0.5586 - val_loss: 0.5639 - val_mae: 0.0950 Epoch 99/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0149 - mae: 0.0863 - val_loss: 0.0150 - val_mae: 0.0865 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.0954 - loss: 0.5580 - val_loss: 0.5641 - val_mae: 0.0950 Epoch 100/100 -157/157 [==============================] - 7s 44ms/step - loss: 0.0150 - mae: 0.0873 - val_loss: 0.0153 - val_mae: 0.0881 -40/40 [==============================] - 1s 15ms/step - loss: 0.0154 - mae: 0.0882 -Loss: 0.02 -MAE: 0.09 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 13s 80ms/step - mae: 0.0955 - loss: 0.5587 - val_loss: 0.5639 - val_mae: 0.0951 + 40/40 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - mae: 0.0955 - loss: 0.5684 +Loss: 0.57 +MAE: 0.10 ```
@@ -1366,35 +1367,52 @@ for layer in downstream_model.layers[:-1]: downstream_model.summary() ``` -
-``` -Model: "linear_probe_model" -_________________________________________________________________ - Layer (type) Output Shape Param # -================================================================= - patches_1 (Patches) (None, 64, 108) 0 - - patch_encoder_1 (PatchEncod (None, 64, 128) 22252 - er) - - mae_encoder (Functional) (None, None, 128) 1981696 - - batch_normalization (BatchN (None, 64, 128) 512 - ormalization) - - global_average_pooling1d (G (None, 128) 0 - lobalAveragePooling1D) - - dense_19 (Dense) (None, 10) 1290 - -================================================================= -Total params: 2,005,750 -Trainable params: 1,290 -Non-trainable params: 2,004,460 -_________________________________________________________________ -``` -
+
Model: "linear_probe_model"
+
+ + + + +
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
+┃ Layer (type)                    ┃ Output Shape              ┃    Param # ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
+│ patches_1 (Patches)             │ (None, 64, 108)           │          0 │
+├─────────────────────────────────┼───────────────────────────┼────────────┤
+│ patch_encoder_1 (PatchEncoder)  │ (None, 64, 128)           │     22,144 │
+├─────────────────────────────────┼───────────────────────────┼────────────┤
+│ mae_encoder (Functional)        │ (None, 64, 128)           │  1,981,696 │
+├─────────────────────────────────┼───────────────────────────┼────────────┤
+│ batch_normalization             │ (None, 64, 128)           │        512 │
+│ (BatchNormalization)            │                           │            │
+├─────────────────────────────────┼───────────────────────────┼────────────┤
+│ global_average_pooling1d        │ (None, 128)               │          0 │
+│ (GlobalAveragePooling1D)        │                           │            │
+├─────────────────────────────────┼───────────────────────────┼────────────┤
+│ dense_20 (Dense)                │ (None, 10)                │      1,290 │
+└─────────────────────────────────┴───────────────────────────┴────────────┘
+
+ + + + +
 Total params: 2,005,642 (7.65 MB)
+
+ + + + +
 Trainable params: 1,290 (5.04 KB)
+
+ + + + +
 Non-trainable params: 2,004,352 (7.65 MB)
+
+ + + We are using average pooling to extract learned representations from the MAE encoder. Another approach would be to use a learnable dummy token inside the encoder during pretraining (resembling the [CLS] token). Then we can extract representations from that @@ -1457,107 +1475,112 @@ print(f"Accuracy on the test set: {accuracy}%.")
``` Epoch 1/50 -157/157 [==============================] - 11s 43ms/step - loss: 2.2131 - accuracy: 0.1838 - val_loss: 2.0249 - val_accuracy: 0.2986 + 7/157 ━━━━━━━━━━━━━━━━━━━━ 3s 21ms/step - accuracy: 0.1183 - loss: 3.3939 + +WARNING: All log messages before absl::InitializeLog() is called are written to STDERR +I0000 00:00:1700264823.481598 64012 device_compiler.h:187] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process. + + 157/157 ━━━━━━━━━━━━━━━━━━━━ 70s 242ms/step - accuracy: 0.1967 - loss: 2.6073 - val_accuracy: 0.3631 - val_loss: 1.7846 Epoch 2/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.9065 - accuracy: 0.3498 - val_loss: 1.7813 - val_accuracy: 0.3913 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 35ms/step - accuracy: 0.3521 - loss: 1.8063 - val_accuracy: 0.3677 - val_loss: 1.7301 Epoch 3/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.7443 - accuracy: 0.3995 - val_loss: 1.6705 - val_accuracy: 0.4195 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.3580 - loss: 1.7580 - val_accuracy: 0.3649 - val_loss: 1.7326 Epoch 4/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.6645 - accuracy: 0.4201 - val_loss: 1.6107 - val_accuracy: 0.4344 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.3617 - loss: 1.7471 - val_accuracy: 0.3810 - val_loss: 1.7353 Epoch 5/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.6169 - accuracy: 0.4320 - val_loss: 1.5747 - val_accuracy: 0.4435 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 6s 35ms/step - accuracy: 0.3547 - loss: 1.7728 - val_accuracy: 0.3526 - val_loss: 1.8496 Epoch 6/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.5843 - accuracy: 0.4364 - val_loss: 1.5476 - val_accuracy: 0.4496 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 6s 35ms/step - accuracy: 0.3546 - loss: 1.7866 - val_accuracy: 0.3896 - val_loss: 1.7583 Epoch 7/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.5634 - accuracy: 0.4418 - val_loss: 1.5294 - val_accuracy: 0.4540 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 6s 37ms/step - accuracy: 0.3587 - loss: 1.7924 - val_accuracy: 0.3674 - val_loss: 1.7729 Epoch 8/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.5462 - accuracy: 0.4452 - val_loss: 1.5158 - val_accuracy: 0.4575 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 6s 38ms/step - accuracy: 0.3616 - loss: 1.7912 - val_accuracy: 0.3685 - val_loss: 1.7928 Epoch 9/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.5365 - accuracy: 0.4468 - val_loss: 1.5068 - val_accuracy: 0.4602 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 6s 36ms/step - accuracy: 0.3707 - loss: 1.7543 - val_accuracy: 0.3568 - val_loss: 1.7943 Epoch 10/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.5237 - accuracy: 0.4541 - val_loss: 1.4971 - val_accuracy: 0.4616 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.3719 - loss: 1.7451 - val_accuracy: 0.3859 - val_loss: 1.7230 Epoch 11/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.5171 - accuracy: 0.4539 - val_loss: 1.4902 - val_accuracy: 0.4620 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.3781 - loss: 1.7384 - val_accuracy: 0.3711 - val_loss: 1.7608 Epoch 12/50 -157/157 [==============================] - 6s 37ms/step - loss: 1.5127 - accuracy: 0.4552 - val_loss: 1.4850 - val_accuracy: 0.4640 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 6s 35ms/step - accuracy: 0.3791 - loss: 1.7249 - val_accuracy: 0.4004 - val_loss: 1.6961 Epoch 13/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.5027 - accuracy: 0.4590 - val_loss: 1.4796 - val_accuracy: 0.4669 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.3818 - loss: 1.7303 - val_accuracy: 0.3501 - val_loss: 1.8506 Epoch 14/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4985 - accuracy: 0.4587 - val_loss: 1.4747 - val_accuracy: 0.4673 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.3841 - loss: 1.7179 - val_accuracy: 0.3810 - val_loss: 1.8033 Epoch 15/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4975 - accuracy: 0.4588 - val_loss: 1.4694 - val_accuracy: 0.4694 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.3818 - loss: 1.7172 - val_accuracy: 0.4168 - val_loss: 1.6507 Epoch 16/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4933 - accuracy: 0.4596 - val_loss: 1.4661 - val_accuracy: 0.4698 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 6s 36ms/step - accuracy: 0.3851 - loss: 1.7059 - val_accuracy: 0.3806 - val_loss: 1.7581 Epoch 17/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4889 - accuracy: 0.4608 - val_loss: 1.4628 - val_accuracy: 0.4721 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.3747 - loss: 1.7356 - val_accuracy: 0.4094 - val_loss: 1.6466 Epoch 18/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4869 - accuracy: 0.4659 - val_loss: 1.4623 - val_accuracy: 0.4721 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 35ms/step - accuracy: 0.3828 - loss: 1.7221 - val_accuracy: 0.4015 - val_loss: 1.6757 Epoch 19/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4826 - accuracy: 0.4639 - val_loss: 1.4585 - val_accuracy: 0.4716 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.3889 - loss: 1.6939 - val_accuracy: 0.4102 - val_loss: 1.6392 Epoch 20/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4813 - accuracy: 0.4653 - val_loss: 1.4559 - val_accuracy: 0.4743 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.3943 - loss: 1.6857 - val_accuracy: 0.4028 - val_loss: 1.6518 Epoch 21/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4824 - accuracy: 0.4644 - val_loss: 1.4542 - val_accuracy: 0.4746 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.3870 - loss: 1.6970 - val_accuracy: 0.3949 - val_loss: 1.7283 Epoch 22/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4768 - accuracy: 0.4667 - val_loss: 1.4526 - val_accuracy: 0.4757 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.3893 - loss: 1.6838 - val_accuracy: 0.4207 - val_loss: 1.6292 Epoch 23/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4775 - accuracy: 0.4644 - val_loss: 1.4507 - val_accuracy: 0.4751 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 35ms/step - accuracy: 0.4005 - loss: 1.6606 - val_accuracy: 0.4152 - val_loss: 1.6320 Epoch 24/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4750 - accuracy: 0.4670 - val_loss: 1.4481 - val_accuracy: 0.4756 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.3978 - loss: 1.6556 - val_accuracy: 0.4042 - val_loss: 1.6657 Epoch 25/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4726 - accuracy: 0.4663 - val_loss: 1.4467 - val_accuracy: 0.4767 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4029 - loss: 1.6464 - val_accuracy: 0.4198 - val_loss: 1.6033 Epoch 26/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4706 - accuracy: 0.4681 - val_loss: 1.4450 - val_accuracy: 0.4781 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.3974 - loss: 1.6638 - val_accuracy: 0.4278 - val_loss: 1.5731 Epoch 27/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4660 - accuracy: 0.4706 - val_loss: 1.4456 - val_accuracy: 0.4766 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 6s 37ms/step - accuracy: 0.4035 - loss: 1.6370 - val_accuracy: 0.4302 - val_loss: 1.5663 Epoch 28/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4664 - accuracy: 0.4707 - val_loss: 1.4443 - val_accuracy: 0.4776 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4027 - loss: 1.6349 - val_accuracy: 0.4458 - val_loss: 1.5349 Epoch 29/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4678 - accuracy: 0.4674 - val_loss: 1.4411 - val_accuracy: 0.4802 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4054 - loss: 1.6196 - val_accuracy: 0.4349 - val_loss: 1.5709 Epoch 30/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4654 - accuracy: 0.4704 - val_loss: 1.4411 - val_accuracy: 0.4801 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 35ms/step - accuracy: 0.4070 - loss: 1.6061 - val_accuracy: 0.4297 - val_loss: 1.5578 Epoch 31/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4655 - accuracy: 0.4702 - val_loss: 1.4402 - val_accuracy: 0.4787 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4105 - loss: 1.6172 - val_accuracy: 0.4250 - val_loss: 1.5735 Epoch 32/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4620 - accuracy: 0.4735 - val_loss: 1.4402 - val_accuracy: 0.4781 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4197 - loss: 1.5960 - val_accuracy: 0.4259 - val_loss: 1.5677 Epoch 33/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4668 - accuracy: 0.4699 - val_loss: 1.4397 - val_accuracy: 0.4783 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4156 - loss: 1.5989 - val_accuracy: 0.4400 - val_loss: 1.5395 Epoch 34/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4619 - accuracy: 0.4724 - val_loss: 1.4382 - val_accuracy: 0.4793 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 35ms/step - accuracy: 0.4214 - loss: 1.5862 - val_accuracy: 0.4486 - val_loss: 1.5237 Epoch 35/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4652 - accuracy: 0.4697 - val_loss: 1.4374 - val_accuracy: 0.4800 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4208 - loss: 1.5763 - val_accuracy: 0.4188 - val_loss: 1.5925 Epoch 36/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4618 - accuracy: 0.4707 - val_loss: 1.4372 - val_accuracy: 0.4794 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4227 - loss: 1.5803 - val_accuracy: 0.4525 - val_loss: 1.5174 Epoch 37/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4606 - accuracy: 0.4710 - val_loss: 1.4369 - val_accuracy: 0.4793 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4267 - loss: 1.5700 - val_accuracy: 0.4463 - val_loss: 1.5330 Epoch 38/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4613 - accuracy: 0.4706 - val_loss: 1.4363 - val_accuracy: 0.4806 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 6s 37ms/step - accuracy: 0.4283 - loss: 1.5649 - val_accuracy: 0.4348 - val_loss: 1.5482 Epoch 39/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4631 - accuracy: 0.4713 - val_loss: 1.4361 - val_accuracy: 0.4804 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4332 - loss: 1.5581 - val_accuracy: 0.4486 - val_loss: 1.5251 Epoch 40/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4620 - accuracy: 0.4695 - val_loss: 1.4357 - val_accuracy: 0.4802 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4290 - loss: 1.5596 - val_accuracy: 0.4489 - val_loss: 1.5221 Epoch 41/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4639 - accuracy: 0.4706 - val_loss: 1.4355 - val_accuracy: 0.4801 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4318 - loss: 1.5589 - val_accuracy: 0.4494 - val_loss: 1.5202 Epoch 42/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4588 - accuracy: 0.4735 - val_loss: 1.4352 - val_accuracy: 0.4802 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4317 - loss: 1.5514 - val_accuracy: 0.4505 - val_loss: 1.5184 Epoch 43/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4573 - accuracy: 0.4734 - val_loss: 1.4352 - val_accuracy: 0.4794 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4353 - loss: 1.5504 - val_accuracy: 0.4561 - val_loss: 1.5081 Epoch 44/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4597 - accuracy: 0.4723 - val_loss: 1.4350 - val_accuracy: 0.4796 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4369 - loss: 1.5510 - val_accuracy: 0.4581 - val_loss: 1.5092 Epoch 45/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4572 - accuracy: 0.4741 - val_loss: 1.4349 - val_accuracy: 0.4799 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 6s 35ms/step - accuracy: 0.4379 - loss: 1.5428 - val_accuracy: 0.4555 - val_loss: 1.5099 Epoch 46/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4561 - accuracy: 0.4756 - val_loss: 1.4348 - val_accuracy: 0.4801 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4421 - loss: 1.5475 - val_accuracy: 0.4579 - val_loss: 1.5073 Epoch 47/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4593 - accuracy: 0.4730 - val_loss: 1.4348 - val_accuracy: 0.4801 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4434 - loss: 1.5390 - val_accuracy: 0.4593 - val_loss: 1.5052 Epoch 48/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4613 - accuracy: 0.4733 - val_loss: 1.4348 - val_accuracy: 0.4802 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 34ms/step - accuracy: 0.4418 - loss: 1.5373 - val_accuracy: 0.4600 - val_loss: 1.5038 Epoch 49/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4591 - accuracy: 0.4710 - val_loss: 1.4348 - val_accuracy: 0.4803 + 157/157 ━━━━━━━━━━━━━━━━━━━━ 6s 38ms/step - accuracy: 0.4400 - loss: 1.5367 - val_accuracy: 0.4596 - val_loss: 1.5045 Epoch 50/50 -157/157 [==============================] - 6s 36ms/step - loss: 1.4566 - accuracy: 0.4766 - val_loss: 1.4348 - val_accuracy: 0.4803 -40/40 [==============================] - 1s 17ms/step - loss: 1.4375 - accuracy: 0.4790 -Accuracy on the test set: 47.9%. + 157/157 ━━━━━━━━━━━━━━━━━━━━ 5s 35ms/step - accuracy: 0.4448 - loss: 1.5321 - val_accuracy: 0.4595 - val_loss: 1.5048 + 40/40 ━━━━━━━━━━━━━━━━━━━━ 3s 71ms/step - accuracy: 0.4496 - loss: 1.5088 +Accuracy on the test set: 44.66%. ```