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Tune an experiment with hyper-parameters. (deprecated)

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.


  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),

  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.