class tf.train.StopAtStepHook

See the guide: Training > Training Hooks

Monitor to request stop at a specified step.


__init__(num_steps=None, last_step=None)

Create a StopAtStep Hook.

This hook requests stop after either a number of steps have been executed or a last step has been reached. Only of the two options can be specified.

if num_steps is specified, it indicates the number of steps to execute after begin() is called. If instead last_step is specified, it indicates the last step we want to execute, as passed to the after_run() call.


  • num_steps: Number of steps to execute.
  • last_step: Step after which to stop.


  • ValueError: If one of the arguments is invalid.

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)




Called at the end of session.

The session argument can be used in case the hook wants to run final ops, such as saving a last checkpoint.


  • session: A TensorFlow Session that will be soon closed.

Defined in tensorflow/python/training/