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.
|