Creates early-stopping hook.
tf.estimator.experimental.make_early_stopping_hook(
estimator, should_stop_fn, run_every_secs=60, run_every_steps=None
)
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.
|
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TensorFlow 1 version
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