tf.contrib.learn.learn_runner.run

Make and run an experiment. (deprecated)

tf.contrib.learn.learn_runner.run(
    experiment_fn, output_dir=None, schedule=None, run_config=None, hparams=None
)

It creates an Experiment by calling experiment_fn. Then it calls the function named as schedule of the Experiment.

If schedule is not provided, then the default schedule for the current task type is used. The defaults are as follows:

'ps' maps to 'serve'
'worker' maps to 'train'
'master' maps to 'local_run'

If the experiment's config does not include a task type, then an exception is raised.

Example with run_config (Recommended):

  def _create_my_experiment(run_config, hparams):

      # You can change a subset of the run_config properties as
      #   run_config = run_config.replace(save_checkpoints_steps=500)

      return tf.contrib.learn.Experiment(
        estimator=my_estimator(config=run_config, hparams=hparams),
        train_input_fn=my_train_input,
        eval_input_fn=my_eval_input)

  learn_runner.run(
    experiment_fn=_create_my_experiment,
    run_config=run_config_lib.RunConfig(model_dir="some/output/dir"),
    schedule="train_and_evaluate",
    hparams=_create_default_hparams())

or simply as

  learn_runner.run(
    experiment_fn=_create_my_experiment,
    run_config=run_config_lib.RunConfig(model_dir="some/output/dir"))

if hparams is not used by the Estimator. On a single machine, schedule defaults to train_and_evaluate.

Example with output_dir (deprecated):

  def _create_my_experiment(output_dir):
      return tf.contrib.learn.Experiment(
        estimator=my_estimator(model_dir=output_dir),
        train_input_fn=my_train_input,
        eval_input_fn=my_eval_input)

  learn_runner.run(
    experiment_fn=_create_my_experiment,
    output_dir="some/output/dir",
    schedule="train")

Args: experiment_fn: A function that creates an Experiment. It could be one of the two following signatures:

1) [Deprecated] It accepts an argument `output_dir` which should be used
to create the `Estimator` (passed as `model_dir` to its constructor). It
must return an `Experiment`. For this case, `run_config` and `hparams`
must be None.
2) It accepts two arguments `run_config` and `hparams`, which should be
used to create the `Estimator` (`run_config` passed as `config` to its
constructor; `hparams` used as the hyper-parameters of the model).
It must return an `Experiment`. For this case, `output_dir` must be None.

output_dir: Base output directory [Deprecated]. schedule: The name of the method in the Experiment to run. run_config: RunConfig instance. The run_config.model_dir must be non-empty. If run_config is set, output_dir must be None. hparams: HParams instance. The default hyper-parameters, which will be passed to the experiment_fn if run_config is not None.

Returns
The return value of function `schedule`.

Raises
`ValueError`	If both `output_dir` and `run_config` are empty or set, `schedule` is None but no task type is set in the built experiment's config, the task type has no default, `run_config.model_dir` is empty or `schedule` doesn't reference a member of `Experiment`.
`TypeError`	`schedule` references non-callable member.

tf.contrib.learn.learn_runner.run

Returns

Raises