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)

原文地址：https://www.cnblogs.com/Libo-Master/p/8926136.html