tf.keras.callbacks.LearningRateScheduler

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Learning rate scheduler.

Inherits From: Callback

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.callbacks.LearningRateScheduler, `tf.compat.v2.keras.callbacks.LearningRateScheduler`

tf.keras.callbacks.LearningRateScheduler(
    schedule, verbose=0
)

Arguments
schedule	a function that takes an epoch index as input (integer, indexed from 0) and returns a new learning rate as output (float).
verbose	int. 0: quiet, 1: update messages.

# This function keeps the learning rate at 0.001 for the first ten epochs
# and decreases it exponentially after that.
def scheduler(epoch):
  if epoch < 10:
    return 0.001
  else:
    return 0.001 * tf.math.exp(0.1 * (10 - epoch))

callback = tf.keras.callbacks.LearningRateScheduler(scheduler)
model.fit(data, labels, epochs=100, callbacks=[callback],
          validation_data=(val_data, val_labels))

Methods

on_batch_begin

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on_batch_begin(
    batch, logs=None
)

A backwards compatibility alias for on_train_batch_begin.

on_batch_end

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on_batch_end(
    batch, logs=None
)

A backwards compatibility alias for on_train_batch_end.

on_epoch_begin

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on_epoch_begin(
    epoch, logs=None
)

Called at the start of an epoch.

Subclasses should override for any actions to run. This function should only be called during TRAIN mode.

Arguments
epoch	integer, index of epoch.
logs	dict. Currently no data is passed to this argument for this method but that may change in the future.

on_epoch_end

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on_epoch_end(
    epoch, logs=None
)

Called at the end of an epoch.

Subclasses should override for any actions to run. This function should only be called during TRAIN mode.

Arguments
epoch	integer, index of epoch.
logs	dict, metric results for this training epoch, and for the validation epoch if validation is performed. Validation result keys are prefixed with val_.

on_predict_batch_begin

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on_predict_batch_begin(
    batch, logs=None
)

Called at the beginning of a batch in predict methods.

Subclasses should override for any actions to run.

Arguments
batch	integer, index of batch within the current epoch.
logs	dict. Has keys batch and size representing the current batch number and the size of the batch.

on_predict_batch_end

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on_predict_batch_end(
    batch, logs=None
)

Called at the end of a batch in predict methods.

Subclasses should override for any actions to run.

Arguments
batch	integer, index of batch within the current epoch.
logs	dict. Metric results for this batch.

on_predict_begin

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on_predict_begin(
    logs=None
)

Called at the beginning of prediction.

Subclasses should override for any actions to run.

Arguments
logs	dict. Currently no data is passed to this argument for this method but that may change in the future.

on_predict_end

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on_predict_end(
    logs=None
)

Called at the end of prediction.

Subclasses should override for any actions to run.

Arguments
logs	dict. Currently no data is passed to this argument for this method but that may change in the future.

on_test_batch_begin

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on_test_batch_begin(
    batch, logs=None
)

Called at the beginning of a batch in evaluate methods.

Also called at the beginning of a validation batch in the fit methods, if validation data is provided.

Subclasses should override for any actions to run.

Arguments
batch	integer, index of batch within the current epoch.
logs	dict. Has keys batch and size representing the current batch number and the size of the batch.

on_test_batch_end

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on_test_batch_end(
    batch, logs=None
)

Called at the end of a batch in evaluate methods.

Also called at the end of a validation batch in the fit methods, if validation data is provided.

Subclasses should override for any actions to run.

Arguments
batch	integer, index of batch within the current epoch.
logs	dict. Metric results for this batch.

on_test_begin

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on_test_begin(
    logs=None
)

Called at the beginning of evaluation or validation.

Subclasses should override for any actions to run.

Arguments
logs	dict. Currently no data is passed to this argument for this method but that may change in the future.

on_test_end

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on_test_end(
    logs=None
)

Called at the end of evaluation or validation.

Subclasses should override for any actions to run.

Arguments
logs	dict. Currently no data is passed to this argument for this method but that may change in the future.

on_train_batch_begin

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on_train_batch_begin(
    batch, logs=None
)

Called at the beginning of a training batch in fit methods.

Subclasses should override for any actions to run.

Arguments
batch	integer, index of batch within the current epoch.
logs	dict. Has keys batch and size representing the current batch number and the size of the batch.

on_train_batch_end

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on_train_batch_end(
    batch, logs=None
)

Called at the end of a training batch in fit methods.

Subclasses should override for any actions to run.

Arguments
batch	integer, index of batch within the current epoch.
logs	dict. Metric results for this batch.

on_train_begin

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on_train_begin(
    logs=None
)

Called at the beginning of training.

Subclasses should override for any actions to run.

Arguments
logs	dict. Currently no data is passed to this argument for this method but that may change in the future.

on_train_end

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on_train_end(
    logs=None
)

Called at the end of training.

Subclasses should override for any actions to run.

Arguments
logs	dict. Currently no data is passed to this argument for this method but that may change in the future.

set_model

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set_model(
    model
)

set_params

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set_params(
    params
)