Python tensorflow.python.training.basic_session_run_hooks.SummarySaverHook() Examples

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Example #1
Source File: monitored_session.py    From deep_image_model with Apache License 2.0 4 votes vote down vote up 
def MonitoredTrainingSession(master='',  # pylint: disable=invalid-name
                             is_chief=True,
                             checkpoint_dir=None,
                             hooks=None,
                             scaffold=None,
                             config=None):
  """Creates a `MonitoredSession` for training.

  For a chief, this utility sets proper session initializer/restorer. It also
  creates hooks related to checkpoint and summary saving. For workers, this
  utility sets proper session creator which waits for the chief to
  inialize/restore.


  Args:
    master: `String` the TensorFlow master to use.
    is_chief: If `True`, it will take care of initialization and recovery the
      underlying TensorFlow session. If `False`, it will wait on a chief to
      initialize or recover the TensorFlow session.
    checkpoint_dir: A string.  Optional path to a directory where to restore
      variables.
    hooks: Optional list of `SessionRunHook` objects.
    scaffold: A `Scaffold` used for gathering or building supportive ops. If
      not specified, a default one is created. It's used to finalize the graph.
    config: `ConfigProto` proto used to configure the session.

  Returns:
    A `MonitoredSession` object.
  """
  hooks = hooks or []
  scaffold = scaffold or Scaffold()
  if not is_chief:
    session_creator = WorkerSessionCreator(
        scaffold=scaffold, master=master, config=config)
  else:
    session_creator = ChiefSessionCreator(
        scaffold=scaffold,
        checkpoint_dir=checkpoint_dir,
        master=master,
        config=config)
    hooks.extend([
        basic_session_run_hooks.StepCounterHook(output_dir=checkpoint_dir),
        basic_session_run_hooks.SummarySaverHook(
            scaffold=scaffold, save_steps=100, output_dir=checkpoint_dir),
        basic_session_run_hooks.CheckpointSaverHook(
            checkpoint_dir, save_secs=600, scaffold=scaffold),
    ])

  return MonitoredSession(session_creator=session_creator, hooks=hooks)