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Tune an experiment with hyper-parameters. (deprecated)
tf.contrib.learn.learn_runner.tune(
experiment_fn, tuner
)
It iterates trials by running the Experiment for each trial with the
corresponding hyper-parameters. For each trial, it retrieves the
hyper-parameters from tuner, creates an Experiment by calling experiment_fn,
and then reports the measure back to tuner.
Example:
def _create_my_experiment(run_config, hparams):
hidden_units = [hparams.unit_per_layer] * hparams.num_hidden_layers
return tf.contrib.learn.Experiment(
estimator=DNNClassifier(config=run_config, hidden_units=hidden_units),
train_input_fn=my_train_input,
eval_input_fn=my_eval_input)
tuner = create_tuner(study_configuration, objective_key)
learn_runner.tune(experiment_fn=_create_my_experiment, tuner)
Args:
experiment_fn: A function that creates an Experiment. It should accept an
argument run_config which should be used to create the Estimator (
passed as config to its constructor), and an argument hparams, which
should be used for hyper-parameters tuning. It must return an
Experiment.
tuner: A Tuner instance.