LambdaCallback

Original Link : https://keras.io/api/callbacks/lambda_callback/
Last Checked at : 2024-11-25

`LambdaCallback` class

keras.callbacks.LambdaCallback(
    on_epoch_begin=None,
    on_epoch_end=None,
    on_train_begin=None,
    on_train_end=None,
    on_train_batch_begin=None,
    on_train_batch_end=None,
    **kwargs
)

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

This callback is constructed with anonymous functions that will be called at the appropriate time (during Model.{fit | evaluate | predict}). Note that the callbacks expects positional arguments, as:

on_epoch_begin and on_epoch_end expect two positional arguments: epoch, logs
on_train_begin and on_train_end expect one positional argument: logs
on_train_batch_begin and on_train_batch_end expect two positional arguments: batch, logs
See Callback class definition for the full list of functions and their expected arguments.

Arguments

on_epoch_begin: called at the beginning of every epoch.
on_epoch_end: called at the end of every epoch.
on_train_begin: called at the beginning of model training.
on_train_end: called at the end of model training.
on_train_batch_begin: called at the beginning of every train batch. -on_train_batch_end: called at the end of every train batch.
**kwargs: Any function in Callback that you want to override by passing function_name=function. For example, LambdaCallback(.., on_train_end=train_end_fn). The custom function needs to have same arguments as the ones defined in Callback.

Example

# Print the batch number at the beginning of every batch.
batch_print_callback = LambdaCallback(
    on_train_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']}) + ''),
            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])

LambdaCallback

LambdaCallback class

`LambdaCallback` class