tf.keras.callbacks.LambdaCallback

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

Inherits From: Callback

tf.keras.callbacks.LambdaCallback(
    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
)

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

set_model

View source

set_model(
    model
)

set_params

View source

set_params(
    params
)