Type CheckpointSaverListener
Namespace tensorflow.train
Parent PythonObjectContainer
Interfaces ICheckpointSaverListener
Interface for listeners that take action before or after checkpoint save. `CheckpointSaverListener` triggers only in steps when `CheckpointSaverHook` is
triggered, and provides callbacks at the following points:
- before using the session
- before each call to `Saver.save()`
- after each call to `Saver.save()`
- at the end of session To use a listener, implement a class and pass the listener to a
`CheckpointSaverHook`, as in this example:
A `CheckpointSaverListener` may simply take some action after every
checkpoint save. It is also possible for the listener to use its own schedule
to act less frequently, e.g. based on global_step_value. In this case,
implementors should implement the `end()` method to handle actions related to
the last checkpoint save. But the listener should not act twice if
`after_save()` already handled this last checkpoint save. A `CheckpointSaverListener` can request training to be stopped, by returning
True in `after_save`. Please note that, in replicated distributed training
setting, only `chief` should use this behavior. Otherwise each worker will do
their own evaluation, which may be wasteful of resources.
Show Example
class ExampleCheckpointSaverListener(CheckpointSaverListener):
def begin(self):
# You can add ops to the graph here.
print('Starting the session.')
self.your_tensor =... def before_save(self, session, global_step_value):
print('About to write a checkpoint') def after_save(self, session, global_step_value):
print('Done writing checkpoint.')
if decided_to_stop_training():
return True def end(self, session, global_step_value):
print('Done with the session.') ...
listener = ExampleCheckpointSaverListener()
saver_hook = tf.estimator.CheckpointSaverHook(
checkpoint_dir, listeners=[listener])
with
tf.compat.v1.train.MonitoredTrainingSession(chief_only_hooks=[saver_hook]):
...