Class LambdaCallback
Inherits From: Callback
Defined in tensorflow/python/keras/callbacks.py
.
Callback for creating simple, custom callbacks on-the-fly.
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
andon_epoch_end
expect two positional arguments:epoch
,logs
on_batch_begin
andon_batch_end
expect two positional arguments:batch
,logs
on_train_begin
andon_train_end
expect 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])
__init__
__init__(
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
)
Initialize self. See help(type(self)) for accurate signature.
Methods
tf.keras.callbacks.LambdaCallback.on_batch_begin
on_batch_begin(
batch,
logs=None
)
tf.keras.callbacks.LambdaCallback.on_batch_end
on_batch_end(
batch,
logs=None
)
tf.keras.callbacks.LambdaCallback.on_epoch_begin
on_epoch_begin(
epoch,
logs=None
)
tf.keras.callbacks.LambdaCallback.on_epoch_end
on_epoch_end(
epoch,
logs=None
)
tf.keras.callbacks.LambdaCallback.on_train_batch_begin
on_train_batch_begin(
batch,
logs=None
)
tf.keras.callbacks.LambdaCallback.on_train_batch_end
on_train_batch_end(
batch,
logs=None
)
tf.keras.callbacks.LambdaCallback.on_train_begin
on_train_begin(logs=None)
tf.keras.callbacks.LambdaCallback.on_train_end
on_train_end(logs=None)
tf.keras.callbacks.LambdaCallback.set_model
set_model(model)
tf.keras.callbacks.LambdaCallback.set_params
set_params(params)