tf.keras.callbacks.LambdaCallback

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Class LambdaCallback

Callback for creating simple, custom callbacks on-the-fly.

Inherits From: Callback

Aliases:

• Class tf.compat.v1.keras.callbacks.LambdaCallback
• Class tf.compat.v2.keras.callbacks.LambdaCallback

This callback is constructed with anonymous functions that will be called at the appropriate time. Note that the callbacks expects positional arguments, as:

• on_epoch_begin and on_epoch_end expect two positional arguments: epoch, logs
• on_batch_begin and on_batch_end expect two positional arguments: batch, logs
• on_train_begin and on_train_end expect one positional argument: logs

Arguments:

• on_epoch_begin: called at the beginning of every epoch.
• on_epoch_end: called at the end of every epoch.
• on_batch_begin: called at the beginning of every batch.
• on_batch_end: called at the end of every batch.
• on_train_begin: called at the beginning of model training.
• on_train_end: called at the end of model training.

Example:

# Print the batch number at the beginning of every batch.
batch_print_callback = LambdaCallback(
    on_batch_begin=lambda batch,logs: print(batch))

# Stream the epoch loss to a file in JSON format. The file content
# is not well-formed JSON but rather has a JSON object per line.
import json
json_log = open('loss_log.json', mode='wt', buffering=1)
json_logging_callback = LambdaCallback(
    on_epoch_end=lambda epoch, logs: json_log.write(
        json.dumps({'epoch': epoch, 'loss': logs['loss']}) + '\n'),
    on_train_end=lambda logs: json_log.close()
)

# Terminate some processes after having finished model training.
processes = ...
cleanup_callback = LambdaCallback(
    on_train_end=lambda logs: [
        p.terminate() for p in processes if p.is_alive()])

model.fit(...,
          callbacks=[batch_print_callback,
                     json_logging_callback,
                     cleanup_callback])

__init__

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__init__(
    on_epoch_begin=None,
    on_epoch_end=None,
    on_batch_begin=None,
    on_batch_end=None,
    on_train_begin=None,
    on_train_end=None,
    **kwargs
)

Initialize self. See help(type(self)) for accurate signature.

Methods

tf.keras.callbacks.LambdaCallback.on_batch_begin

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on_batch_begin(
    batch,
    logs=None
)

A backwards compatibility alias for on_train_batch_begin.

tf.keras.callbacks.LambdaCallback.on_batch_end

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on_batch_end(
    batch,
    logs=None
)

A backwards compatibility alias for on_train_batch_end.

tf.keras.callbacks.LambdaCallback.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.

tf.keras.callbacks.LambdaCallback.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_.

tf.keras.callbacks.LambdaCallback.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.

tf.keras.callbacks.LambdaCallback.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.

tf.keras.callbacks.LambdaCallback.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.

tf.keras.callbacks.LambdaCallback.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.

tf.keras.callbacks.LambdaCallback.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.

tf.keras.callbacks.LambdaCallback.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.

tf.keras.callbacks.LambdaCallback.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.

tf.keras.callbacks.LambdaCallback.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.

tf.keras.callbacks.LambdaCallback.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.

tf.keras.callbacks.LambdaCallback.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.

Arguments:

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

tf.keras.callbacks.LambdaCallback.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.

tf.keras.callbacks.LambdaCallback.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.

tf.keras.callbacks.LambdaCallback.set_model

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set_model(model)

tf.keras.callbacks.LambdaCallback.set_params

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set_params(params)