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.
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.