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大牛时代配资Creates early-stopping hook.


  • tf.compat.v1.estimator.experimental.make_early_stopping_hook
  • tf.compat.v2.estimator.experimental.make_early_stopping_hook

Returns a SessionRunHook that stops training when should_stop_fn returns True.

Usage example:

estimator = ...
hook = early_stopping.make_early_stopping_hook(
    estimator, should_stop_fn=make_stop_fn(...))
train_spec = tf.estimator.TrainSpec(..., hooks=[hook])
tf.estimator.train_and_evaluate(estimator, train_spec, ...)

Caveat: Current implementation supports early-stopping both training and evaluation in local mode. In distributed mode, training can be stopped but evaluation (where it's a separate job) will indefinitely wait for new model checkpoints to evaluate, so you will need other means to detect and stop it. Early-stopping evaluation in distributed mode requires changes in train_and_evaluate API and will be addressed in a future revision.


  • estimator: A tf.estimator.Estimator instance.
  • should_stop_fn: callable, function that takes no arguments and returns a bool. If the function returns True, stopping will be initiated by the chief.
  • run_every_secs: If specified, calls should_stop_fn at an interval of run_every_secs seconds. Defaults to 60 seconds. Either this or run_every_steps must be set.
  • run_every_steps: If specified, calls should_stop_fn every run_every_steps steps. Either this or run_every_secs must be set.


A SessionRunHook that periodically executes should_stop_fn and initiates early stopping if the function returns True.


  • TypeError: If estimator is not of type tf.estimator.Estimator.
  • ValueError: If both run_every_secs and run_every_steps are set.

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