tf.keras.callbacks.Callback  |  TensorFlow v2.16.1 (original) (raw)

Base class used to build new callbacks.

tf.keras.callbacks.Callback()

Used in the notebooks

Used in the guide Used in the tutorials
Migrate the fault tolerance mechanism Migrate LoggingTensorHook and StopAtStepHook to Keras callbacks Migrate early stopping Migrate SessionRunHook to Keras callbacks Distributed training with Keras Image segmentation Uncertainty-aware Deep Learning with SNGP Assess privacy risks with the TensorFlow Privacy Report Image captioning with visual attention

Callbacks can be passed to keras methods such as fit(), evaluate(), andpredict() in order to hook into the various stages of the model training, evaluation, and inference lifecycle.

To create a custom callback, subclass keras.callbacks.Callback and override the method associated with the stage of interest.

Example:

training_finished = False class MyCallback(Callback): def on_train_end(self, logs=None): global training_finished training_finished = True model = Sequential([ layers.Dense(1, input_shape=(1,))]) model.compile(loss='mean_squared_error') model.fit(np.array([[1.0]]), np.array([[1.0]]), callbacks=[MyCallback()]) assert training_finished == True

If you want to use Callback objects in a custom training loop:

  1. You should pack all your callbacks into a single callbacks.CallbackListso they can all be called together.
  2. You will need to manually call all the on_* methods at the appropriate locations in your loop. Like this:

Example:

callbacks =  keras.callbacks.CallbackList([...])
callbacks.append(...)
callbacks.on_train_begin(...)
for epoch in range(EPOCHS):
    callbacks.on_epoch_begin(epoch)
    for i, data in dataset.enumerate():
    callbacks.on_train_batch_begin(i)
    batch_logs = model.train_step(data)
    callbacks.on_train_batch_end(i, batch_logs)
    epoch_logs = ...
    callbacks.on_epoch_end(epoch, epoch_logs)
final_logs=...
callbacks.on_train_end(final_logs)

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 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.

Args
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.

Args
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, 'accuracy': 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 inModel is set to N, this method will only be called everyN batches.

Args
batch Integer, index of batch within the current epoch.
logs Dict. Currently no data is passed to this argument for this method but that may change in the future.

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 inModel is set to N, this method will only be called everyN batches.

Args
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.

Args
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.

Args
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 fitmethods, if validation data is provided.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile inModel is set to N, this method will only be called everyN batches.

Args
batch Integer, index of batch within the current epoch.
logs Dict. Currently no data is passed to this argument for this method but that may change in the future.

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 fitmethods, if validation data is provided.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile inModel is set to N, this method will only be called everyN batches.

Args
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.

Args
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.

Args
logs Dict. Currently the output of the last call toon_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 inModel is set to N, this method will only be called everyN batches.

Args
batch Integer, index of batch within the current epoch.
logs Dict. Currently no data is passed to this argument for this method but that may change in the future.

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 inModel is set to N, this method will only be called everyN batches.

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

Args
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.

Args
logs Dict. Currently the output of the last call toon_epoch_end() 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
)