tf.contrib.learn.learn_runner.tune

Tune an experiment with hyper-parameters. (deprecated)

tf.contrib.learn.learn_runner.tune(
    experiment_fn, tuner
)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.estimator.train_and_evaluate.

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.