tf.keras.callbacks.Callback

Base class used to build new callbacks.

tf.keras.callbacks.Callback()

Used in the notebooks

Used in the guide	Used in the tutorials
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Callbacks can be passed to keras methods such as fit(), evaluate(), and predict() 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.CallbackList so 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

View source

on_batch_begin(
    batch, logs=None
)

A backwards compatibility alias for on_train_batch_begin.

on_batch_end

View source

on_batch_end(
    batch, logs=None
)

A backwards compatibility alias for on_train_batch_end.

on_epoch_begin

View source

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

View source

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

View source

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 Model is set to N, this method will only be called every N 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

View source

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 Model is set to N, this method will only be called every N batches.

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

on_predict_begin

View source

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

View source

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 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 Model is set to N, this method will only be called every N 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

View source

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 Model is set to N, this method will only be called every N batches.

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

on_test_begin

View source

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

View source

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 to on_test_batch_end() is passed to this argument for this method but that may change in the future.

on_train_batch_begin

View source

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 Model is set to N, this method will only be called every N 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

View source

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 Model is set to N, this method will only be called every N batches.

Args
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.

Args
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.

Args
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
)