|TensorFlow 1 version||View source on GitHub|
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 )
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.
str, metric to track. "loss", "accuracy", etc.
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.
int, stop is never requested if global step is less than this value. Defaults to 0.
run_every_secs: If specified, calls
should_stop_fnat an interval of
run_every_secsseconds. Defaults to 60 seconds. Either this or
run_every_stepsmust be set.
run_every_steps: If specified, calls
run_every_stepssteps. Either this or
run_every_secsmust be set.
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.