tf.keras.callbacks.Callback

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Abstract base class used to build new callbacks.

tf.keras.callbacks.Callback()

Attributes:

• params: dict. Training parameters (eg. verbosity, batch size, number of epochs...).
• model: instance of keras.models.Model. Reference of the model being trained.
• validation_data: Deprecated. Do not use.

The logs dictionary that callback methods take as argument will contain keys for quantities relevant to the current batch or epoch.

Currently, the .fit() method of the Model class will include the following quantities in the logs that it passes to its callbacks:

on_epoch_end: logs include `acc` and `loss`, and
    optionally include `val_loss`
    (if validation is enabled in `fit`), and `val_acc`
    (if validation and accuracy monitoring are enabled).
on_batch_begin: logs include `size`,
    the number of samples in the current batch.
on_batch_end: logs include `loss`, and optionally `acc`
    (if accuracy monitoring is enabled).

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
)