tf.keras.callbacks.LambdaCallback

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

Inherits From: Callback

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.callbacks.LambdaCallback

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

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
)