tf.keras.callbacks

TensorFlow 1 version

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Abstract base class used to build new callbacks.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.callbacks.Callback

tf.keras.callbacks.Callback()

Used in the notebooks

Used in the guide	Used in the tutorials
Writing your own callbacks Training and evaluation with the built-in methods	Distributed training with Keras Image segmentation Transfer learning with TensorFlow Hub Introduction to the Keras Tuner

The logs dictionary that callback methods take as argument will contain keys for quantities relevant to the current batch or epoch (see method-specific docstrings).

Attributes
`params`	Dict. Training parameters (eg. verbosity, batch size, number of epochs...).
`model`	Instance of `keras.models.Model`. Reference of the model being trained.

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_`. For training epoch, the values of the `Model`'s metrics are returned. Example : `{'loss': 0.2, 'acc': 0.7}`.

`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.

Note that if the steps_per_execution argument to compile in tf.keras.Model is set to N, this method will only be called every N batches.

Arguments
`batch`	Integer, index of batch within the current epoch.
`logs`	Dict, contains the return value of `model.predict_step`, it typically returns a dict with a key 'outputs' containing the model's outputs.

`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.

Note that if the steps_per_execution argument to compile in tf.keras.Model is set to N, this method will only be called every N batches.

Arguments
`batch`	Integer, index of batch within the current epoch.
`logs`	Dict. Aggregated metric results up until 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.

Note that if the steps_per_execution argument to compile in tf.keras.Model is set to N, this method will only be called every N batches.

Arguments
`batch`	Integer, index of batch within the current epoch.
`logs`	Dict, contains the return value of `model.test_step`. Typically, the values of the `Model`'s metrics are returned. Example: `{'loss': 0.2, 'accuracy': 0.7}`.

`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.

Note that if the steps_per_execution argument to compile in tf.keras.Model is set to N, this method will only be called every N batches.

Arguments
`batch`	Integer, index of batch within the current epoch.
`logs`	Dict. Aggregated metric results up until 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 the output of the last call to `on_test_batch_end()` 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.

Note that if the steps_per_execution argument to compile in tf.keras.Model is set to N, this method will only be called every N batches.

Arguments
`batch`	Integer, index of batch within the current epoch.
`logs`	Dict, contains the return value of `model.train_step`. Typically, the values of the `Model`'s metrics are returned. Example: `{'loss': 0.2, 'accuracy': 0.7}`.

`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.

Note that if the steps_per_execution argument to compile in tf.keras.Model is set to N, this method will only be called every N batches.

Arguments
`batch`	Integer, index of batch within the current epoch.
`logs`	Dict. Aggregated metric results up until this batch.

`on_train_begin`

View source

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`

View source

on_train_end(
    logs=None
)

Called at the end of training.

Subclasses should override for any actions to run.

Arguments
`logs`	Dict. Currently the output of the last call to `on_epoch_end()` is passed to this argument for this method but that may change in the future.

`set_model`

View source

set_model(
    model
)

`set_params`

View source

set_params(
    params
)

TensorFlow

tf

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tf.distribute

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tf.sysconfig

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tf.keras / callbacks / callbacks.Overview

View aliases

Used in the notebooks

Attributes

Methods

on_batch_begin

on_batch_end

on_epoch_begin

on_epoch_end

on_predict_batch_begin

on_predict_batch_end

on_predict_begin

on_predict_end

on_test_batch_begin

on_test_batch_end

on_test_begin

on_test_end

on_train_batch_begin

on_train_batch_end

on_train_begin

on_train_end

set_model

set_params

`on_batch_begin`

`on_batch_end`

`on_epoch_begin`

`on_epoch_end`

`on_predict_batch_begin`

`on_predict_batch_end`

`on_predict_begin`

`on_predict_end`

`on_test_batch_begin`

`on_test_batch_end`

`on_test_begin`

`on_test_end`

`on_train_batch_begin`

`on_train_batch_end`

`on_train_begin`

`on_train_end`

`set_model`

`set_params`