LambdaCallback
LambdaCallback class
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
)
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_beginandon_epoch_endexpect two positional arguments:epoch,logson_batch_beginandon_batch_endexpect two positional arguments:batch,logson_train_beginandon_train_endexpect 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])