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