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return tf.estimator.Estimator(
    model_fn=model_fn,  # First-class function
    params=params,  # HParams
    config=run_config  # RunConfig
)

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experiment = tf.contrib.learn.Experiment(
    estimator=estimator,  # Estimator
    train_input_fn=train_input_fn,  # First-class function
    eval_input_fn=eval_input_fn,  # First-class function
    train_steps=params.train_steps,  # Minibatch steps
    min_eval_frequency=params.min_eval_frequency,  # Eval frequency
    train_monitors=[train_input_hook],  # Hooks for training
    eval_hooks=[eval_input_hook],  # Hooks for evaluation
    eval_steps=None  # Use evaluation feeder until its empty
)

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learn_runner.run(
    experiment_fn=experiment_fn,  # First-class function
    run_config=run_config,  # RunConfig
    schedule="train_and_evaluate",  # What to run
    hparams=params  # HParams
)

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# Define the training inputs
def get_train_inputs(batch_size, mnist_data):
    """Return the input function to get the training data.

    Args:
        batch_size (int): Batch size of training iterator that is returned
                          by the input function.
        mnist_data (Object): Object holding the loaded mnist data.

    Returns:

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class IteratorInitializerHook(tf.train.SessionRunHook):
    """Hook to initialise data iterator after Session is created."""

    def __init__(self):
        super(IteratorInitializerHook, self).__init__()
        self.iterator_initializer_func = None

    def after_create_session(self, session, coord):
        """Initialise the iterator after the session has been created."""
        self.iterator_initializer_func(session)

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return tf.estimator.Estimator(
    model_fn=model_fn,
    config=config,
    params=params,
)