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Recurrent Neural Networks
data_dir = "./data"
logs_dir = os.path.join("./logs")
best_model_file = os.path.join(data_dir, "best_model.h5")
checkpoint = tf.keras.callbacks.ModelCheckpoint(best_model_file,
save_weights_only=True,
save_best_only=True)
tensorboard = tf.keras.callbacks.TensorBoard(log_dir=logs_dir)
num_epochs = 10
history = model.fit(train_dataset, epochs=num_epochs,
validation_data=val_dataset,
callbacks=[checkpoint, tensorboard])
As you can see from the output, training set accuracy goes to 99.8 % and best
validation set accuracy goes to about 78.5%. Figure 5 shows TensorBoard plots of
accuracy and loss for the training and validation datasets:
Epoch 1/10
29/29 [==============================] - 7s 239ms/step - loss: 0.6918 -
accuracy: 0.5148 - val_loss: 0.6940 - val_accuracy: 0.4750
Epoch 2/10
29/29 [==============================] - 3s 98ms/step - loss: 0.6382 -
accuracy: 0.5928 - val_loss: 0.6311 - val_accuracy: 0.6000
Epoch 3/10
29/29 [==============================] - 3s 100ms/step - loss: 0.3661 -
accuracy: 0.8250 - val_loss: 0.4894 - val_accuracy: 0.7600
Epoch 4/10
29/29 [==============================] - 3s 99ms/step - loss: 0.1567 -
accuracy: 0.9564 - val_loss: 0.5469 - val_accuracy: 0.7750
Epoch 5/10
29/29 [==============================] - 3s 99ms/step - loss: 0.0768 -
accuracy: 0.9875 - val_loss: 0.6197 - val_accuracy: 0.7450
Epoch 6/10
29/29 [==============================] - 3s 100ms/step - loss: 0.0387 -
accuracy: 0.9937 - val_loss: 0.6529 - val_accuracy: 0.7500
Epoch 7/10
29/29 [==============================] - 3s 99ms/step - loss: 0.0215 -
accuracy: 0.9989 - val_loss: 0.7597 - val_accuracy: 0.7550
Epoch 8/10
29/29 [==============================] - 3s 100ms/step - loss: 0.0196 -
accuracy: 0.9987 - val_loss: 0.6745 - val_accuracy: 0.7450
Epoch 9/10
29/29 [==============================] - 3s 99ms/step - loss: 0.0136 -
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