tf.estimator.experimental.stop_if_no_increase_hook

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Creates hook to stop if metric does not increase within given max steps.

tf.estimator.experimental.stop_if_no_increase_hook(
    estimator,
    metric_name,
    max_steps_without_increase,
    eval_dir=None,
    min_steps=0,
    run_every_secs=60,
    run_every_steps=None
)

Usage example:

estimator = ...
# Hook to stop training if accuracy does not increase in over 100000 steps.
hook = early_stopping.stop_if_no_increase_hook(estimator, "accuracy", 100000)
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.

Args:

  • estimator: A tf.estimator.Estimator instance.
  • metric_name: str, metric to track. "loss", "accuracy", etc.
  • max_steps_without_increase: int, maximum number of training steps with no increase in the given metric.
  • eval_dir: If set, directory containing summary files with eval metrics. By default, estimator.eval_dir() will be used.
  • min_steps: int, stop is never requested if global step is less than this value. Defaults to 0.
  • 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.

Returns:

An early-stopping hook of type SessionRunHook that periodically checks if the given metric shows no increase over given maximum number of training steps, and initiates early stopping if true.

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