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tf.keras.callbacks.LambdaCallback

TensorFlow 1 version View source on GitHub

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

Inherits From: Callback

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

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])

Methods

on_batch_begin

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A backwards compatibility alias for on_train_batch_begin.

on_batch_end

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A backwards compatibility alias for on_train_batch_end.

on_epoch_begin

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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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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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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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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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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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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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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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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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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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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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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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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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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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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_params

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