Creates hook to stop if metric does not decrease within given max steps.
tf.estimator.experimental.stop_if_no_decrease_hook(
estimator, metric_name, max_steps_without_decrease, eval_dir=None, min_steps=0,
run_every_secs=60, run_every_steps=None
)
Usage example:
estimator = ...
# Hook to stop training if loss does not decrease in over 100000 steps.
hook = early_stopping.stop_if_no_decrease_hook(estimator, "loss", 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_decrease
|
int , maximum number of training steps with no
decrease 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 decrease over given maximum number of
training steps, and initiates early stopping if true.
|