class tf.train.SummarySaverHook

See the guide: Training > Training Hooks

Saves summaries every N steps.


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

Initializes a SummarySaver monitor.


  • 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.


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

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.


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

after_run(run_context, run_values)




Defined in tensorflow/python/training/