tf.estimator.experimental.stop_if_higher_hook

View source on GitHub

Creates hook to stop if the given metric is higher than the threshold.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.estimator.experimental.stop_if_higher_hook

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

Usage example:

estimator = ...
# Hook to stop training if accuracy becomes higher than 0.9.
hook = early_stopping.stop_if_higher_hook(estimator, "accuracy", 0.9)
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.
threshold	Numeric threshold for 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 is higher than specified threshold and initiates early stopping if true.