tf.train.SummarySaverHook

Class SummarySaverHook

Inherits From: SessionRunHook

Defined in tensorflow/python/training/basic_session_run_hooks.py.

See the guide: Training > Training Hooks

Saves summaries every N steps.

Methods

__init__

__init__(
    save_steps=None,
    save_secs=None,
    output_dir=None,
    summary_writer=None,
    scaffold=None,
    summary_op=None
)

Initializes a SummarySaverHook.

Args:

  • save_steps: int, save summaries every N steps. Exactly one of save_secs and save_steps should be set.
  • save_secs: int, save summaries every N seconds.
  • output_dir: string, the directory to save the summaries to. Only used if no summary_writer is supplied.
  • summary_writer: SummaryWriter. If None and an output_dir was passed, one will be created accordingly.
  • scaffold: Scaffold to get summary_op if it's not provided.
  • summary_op: Tensor of type string containing the serialized Summary protocol buffer or a list of Tensor. They are most likely an output by TF summary methods like tf.summary.scalar or tf.summary.merge_all. It can be passed in as one tensor; if more than one, they must be passed in as a list.

Raises:

  • ValueError: Exactly one of scaffold or summary_op should be set.

after_create_session

after_create_session(
    session,
    coord
)

Called when new TensorFlow session is created.

This is called to signal the hooks that a new session has been created. This has two essential differences with the situation in which begin is called:

  • When this is called, the graph is finalized and ops can no longer be added to the graph.
  • This method will also be called as a result of recovering a wrapped session, not only at the beginning of the overall session.

Args:

  • session: A TensorFlow Session that has been created.
  • coord: A Coordinator object which keeps track of all threads.

after_run

after_run(
    run_context,
    run_values
)

before_run

before_run(run_context)

begin

begin()

end

end(session=None)