tf.train.CheckpointSaverHook

tf.train.CheckpointSaverHook

class tf.train.CheckpointSaverHook

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

See the guide: Training > Training Hooks

Saves checkpoints every N steps or seconds.

Methods

__init__

__init__(
    checkpoint_dir,
    save_secs=None,
    save_steps=None,
    saver=None,
    checkpoint_basename='model.ckpt',
    scaffold=None,
    listeners=None
)

Initializes a CheckpointSaverHook.

Args:

• checkpoint_dir: str, base directory for the checkpoint files.
• save_secs: int, save every N secs.
• save_steps: int, save every N steps.
• saver: Saver object, used for saving.
• checkpoint_basename: str, base name for the checkpoint files.
• scaffold: Scaffold, use to get saver object.
• listeners: List of CheckpointSaverListener subclass instances. Used for callbacks that run immediately before or after this hook saves the checkpoint.

Raises:

• ValueError: One of save_steps or save_secs should be set.
• ValueError: Exactly one of saver or scaffold 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)