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TensorFlow 1.x and 2.x
• tf.keras.callbacks.LearningRateScheduler: This feature is used
to dynamically change the learning rate during optimization.
• tf.keras.callbacks.EarlyStopping: This feature is used to interrupt
training when validation performance has stopped improving after a while.
• tf.keras.callbacks.TensorBoard: This feature is used to monitor the
model's behavior using TensorBoard.
For example, we have already used TensorBoard as in this example:
callbacks = [
# Write TensorBoard logs to './logs' directory
tf.keras.callbacks.TensorBoard(log_dir='./logs')
]
model.fit(data, labels, batch_size=256, epochs=100,
callbacks=callbacks,
validation_data=(val_data, val_labels))
Saving a model and weights
After training a model, it can be useful to save the weights in a persistent way. This
is easily achieved with the following code fragment, which saves to TensorFlow's
internal format:
# Save weights to a Tensorflow Checkpoint file
model.save_weights('./weights/my_model')
If you want to save in Keras's format, which is portable across multiple backends,
then use:
# Save weights to a HDF5 file
model.save_weights('my_model.h5', save_format='h5')
Weights are easily loaded with:
# Restore the model's state
model.load_weights(file_path)
In addition to weights, a model can be serialized in JSON with:
json_string = model.to_json() # save
model = tf.keras.models.model_from_json(json_string) # restore
If you prefer, a model can be serialized in YAML with:
yaml_string = model.to_yaml() # save
model = tf.keras.models.model_from_yaml(yaml_string) # restore
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