Creates early-stopping hook. (deprecated)
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Compat aliases for migration
See
Migration guide for
more details.
`tf.compat.v1.estimator.experimental.make_early_stopping_hook`
tf.estimator.experimental.make_early_stopping_hook(
estimator, should_stop_fn, run_every_secs=60, run_every_steps=None
)
Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
Returns a SessionRunHook
that stops training when should_stop_fn
returns
True
.
Usage example:
estimator = ...
hook = early_stopping.make_early_stopping_hook(
estimator, should_stop_fn=make_stop_fn(...))
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.
should_stop_fn
callable
, function that takes no arguments and returns a
bool
. If the function returns True
, stopping will be initiated by the
chief.
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
A SessionRunHook
that periodically executes should_stop_fn
and initiates
early stopping if the function returns True
.
Raises
TypeError
If estimator
is not of type tf.estimator.Estimator
.
ValueError
If both run_every_secs
and run_every_steps
are set.