Datasets:
HenryAI
/
KerasCodeExamples.txt

Dataset card Data Studio Files
xet
 Community
text
stringlengths
0
4.99k
)
return model
The first step to transfer learning is to freeze all layers and train only the top layers. For this step, a relatively large learning rate (1e-2) can be used. Note that validation accuracy and loss will usually be better than training accuracy and loss. This is because the regularization is strong, which only suppresse...
Note that the convergence may take up to 50 epochs depending on choice of learning rate. If image augmentation layers were not applied, the validation accuracy may only reach ~60%.
with strategy.scope():
model = build_model(num_classes=NUM_CLASSES)
epochs = 25 # @param {type: \"slider\", min:8, max:80}
hist = model.fit(ds_train, epochs=epochs, validation_data=ds_test, verbose=2)
plot_hist(hist)
Epoch 1/25
187/187 - 33s - loss: 3.5673 - accuracy: 0.3624 - val_loss: 1.0288 - val_accuracy: 0.6957
Epoch 2/25
187/187 - 31s - loss: 1.8503 - accuracy: 0.5232 - val_loss: 0.8439 - val_accuracy: 0.7484
Epoch 3/25
187/187 - 31s - loss: 1.5511 - accuracy: 0.5772 - val_loss: 0.7953 - val_accuracy: 0.7563
Epoch 4/25
187/187 - 31s - loss: 1.4660 - accuracy: 0.5878 - val_loss: 0.8061 - val_accuracy: 0.7535
Epoch 5/25
187/187 - 31s - loss: 1.4143 - accuracy: 0.6034 - val_loss: 0.7850 - val_accuracy: 0.7569
Epoch 6/25
187/187 - 31s - loss: 1.4000 - accuracy: 0.6054 - val_loss: 0.7846 - val_accuracy: 0.7646
Epoch 7/25
187/187 - 31s - loss: 1.3678 - accuracy: 0.6173 - val_loss: 0.7850 - val_accuracy: 0.7682
Epoch 8/25
187/187 - 31s - loss: 1.3286 - accuracy: 0.6222 - val_loss: 0.8142 - val_accuracy: 0.7608
Epoch 9/25
187/187 - 31s - loss: 1.3210 - accuracy: 0.6245 - val_loss: 0.7890 - val_accuracy: 0.7669
Epoch 10/25
187/187 - 31s - loss: 1.3086 - accuracy: 0.6278 - val_loss: 0.8368 - val_accuracy: 0.7575
Epoch 11/25
187/187 - 31s - loss: 1.2877 - accuracy: 0.6315 - val_loss: 0.8309 - val_accuracy: 0.7599
Epoch 12/25
187/187 - 31s - loss: 1.2918 - accuracy: 0.6308 - val_loss: 0.8319 - val_accuracy: 0.7535
Epoch 13/25
187/187 - 31s - loss: 1.2738 - accuracy: 0.6373 - val_loss: 0.8567 - val_accuracy: 0.7576
Epoch 14/25
187/187 - 31s - loss: 1.2837 - accuracy: 0.6410 - val_loss: 0.8004 - val_accuracy: 0.7697
Epoch 15/25
187/187 - 31s - loss: 1.2828 - accuracy: 0.6403 - val_loss: 0.8364 - val_accuracy: 0.7625
Epoch 16/25
187/187 - 31s - loss: 1.2749 - accuracy: 0.6405 - val_loss: 0.8558 - val_accuracy: 0.7565
Epoch 17/25
187/187 - 31s - loss: 1.3022 - accuracy: 0.6352 - val_loss: 0.8361 - val_accuracy: 0.7551
Epoch 18/25
187/187 - 31s - loss: 1.2848 - accuracy: 0.6394 - val_loss: 0.8958 - val_accuracy: 0.7479
Epoch 19/25
187/187 - 31s - loss: 1.2791 - accuracy: 0.6420 - val_loss: 0.8875 - val_accuracy: 0.7509
Epoch 20/25
187/187 - 30s - loss: 1.2834 - accuracy: 0.6416 - val_loss: 0.8653 - val_accuracy: 0.7607
Epoch 21/25
187/187 - 30s - loss: 1.2608 - accuracy: 0.6435 - val_loss: 0.8451 - val_accuracy: 0.7612
Epoch 22/25
187/187 - 30s - loss: 1.2780 - accuracy: 0.6390 - val_loss: 0.9035 - val_accuracy: 0.7486
Epoch 23/25
187/187 - 30s - loss: 1.2742 - accuracy: 0.6473 - val_loss: 0.8837 - val_accuracy: 0.7556
Epoch 24/25
187/187 - 30s - loss: 1.2609 - accuracy: 0.6434 - val_loss: 0.9233 - val_accuracy: 0.7524
Epoch 25/25
187/187 - 31s - loss: 1.2630 - accuracy: 0.6496 - val_loss: 0.9116 - val_accuracy: 0.7584
png
The second step is to unfreeze a number of layers and fit the model using smaller learning rate. In this example we show unfreezing all layers, but depending on specific dataset it may be desireble to only unfreeze a fraction of all layers.
When the feature extraction with pretrained model works good enough, this step would give a very limited gain on validation accuracy. In our case we only see a small improvement, as ImageNet pretraining already exposed the model to a good amount of dogs.
On the other hand, when we use pretrained weights on a dataset that is more different from ImageNet, this fine-tuning step can be crucial as the feature extractor also needs to be adjusted by a considerable amount. Such a situation can be demonstrated if choosing CIFAR-100 dataset instead, where fine-tuning boosts vali...
A side note on freezing/unfreezing models: setting trainable of a Model will simultaneously set all layers belonging to the Model to the same trainable attribute. Each layer is trainable only if both the layer itself and the model containing it are trainable. Hence when we need to partially freeze/unfreeze a model, we ...
def unfreeze_model(model):
# We unfreeze the top 20 layers while leaving BatchNorm layers frozen
for layer in model.layers[-20:]:
if not isinstance(layer, layers.BatchNormalization):
layer.trainable = True
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4)
model.compile(
optimizer=optimizer, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"]
)
unfreeze_model(model)
epochs = 10 # @param {type: \"slider\", min:8, max:50}
hist = model.fit(ds_train, epochs=epochs, validation_data=ds_test, verbose=2)
plot_hist(hist)
Epoch 1/10
187/187 - 33s - loss: 0.9956 - accuracy: 0.7080 - val_loss: 0.7644 - val_accuracy: 0.7856
Epoch 2/10
187/187 - 31s - loss: 0.8885 - accuracy: 0.7352 - val_loss: 0.7696 - val_accuracy: 0.7866
Epoch 3/10
187/187 - 31s - loss: 0.8059 - accuracy: 0.7533 - val_loss: 0.7659 - val_accuracy: 0.7885
Epoch 4/10
187/187 - 32s - loss: 0.7648 - accuracy: 0.7675 - val_loss: 0.7730 - val_accuracy: 0.7866
Epoch 5/10
187/187 - 32s - loss: 0.6982 - accuracy: 0.7833 - val_loss: 0.7691 - val_accuracy: 0.7858
Epoch 6/10