tensorflow API _ 3 (tf.train.polynomial_decay)

学习率的三种调整方式：
固定的，指数的，多项式的

def _configure_learning_rate(num_samples_per_epoch, global_step):
  """Configures the learning rate.

  Args:
    num_samples_per_epoch: The number of samples in each epoch of training.
    global_step: The global_step tensor.

  Returns:
    A `Tensor` representing the learning rate.

  Raises:
    ValueError: if
  """
  decay_steps = int(num_samples_per_epoch / FLAGS.batch_size *
                    FLAGS.num_epochs_per_decay)
  if FLAGS.sync_replicas:
    decay_steps /= FLAGS.replicas_to_aggregate

  if FLAGS.learning_rate_decay_type == 'exponential':
    return tf.train.exponential_decay(FLAGS.learning_rate,
                                      global_step,
                                      decay_steps,
                                      FLAGS.learning_rate_decay_factor,
                                      staircase=True,
                                      name='exponential_decay_learning_rate')
  elif FLAGS.learning_rate_decay_type == 'fixed':
    return tf.constant(FLAGS.learning_rate, name='fixed_learning_rate')
  elif FLAGS.learning_rate_decay_type == 'polynomial':
    return tf.train.polynomial_decay(FLAGS.learning_rate,
                                     global_step,
                                     decay_steps,
                                     FLAGS.end_learning_rate,
                                     power=1.0,
                                     cycle=False,
                                     name='polynomial_decay_learning_rate')
  else:
    raise ValueError('learning_rate_decay_type [%s] was not recognized',
                     FLAGS.learning_rate_decay_type)