View source on GitHub
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View source on GitHub
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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, logson_batch_begin and on_batch_end expect two positional arguments:
batch, logson_train_begin and on_train_end expect one positional argument:
logson_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.# 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])
set_modelset_model(
model
)
set_paramsset_params(
params
)