tensorflow API _ 4 (优化器配置)

"""Configures the optimizer used for training.

Args:
  learning_rate: A scalar or `Tensor` learning rate.

Returns:
  An instance of an optimizer.

Raises:
  ValueError: if FLAGS.optimizer is not recognized.
"""
if FLAGS.optimizer == 'adadelta':
  optimizer = tf.train.AdadeltaOptimizer(
    learning_rate,
    rho=FLAGS.adadelta_rho,
    epsilon=FLAGS.opt_epsilon)
elif FLAGS.optimizer == 'adagrad':
  optimizer = tf.train.AdagradOptimizer(
    learning_rate,
    initial_accumulator_value=FLAGS.adagrad_initial_accumulator_value)
elif FLAGS.optimizer == 'adam':
  optimizer = tf.train.AdamOptimizer(
    learning_rate,
    beta1=FLAGS.adam_beta1,
    beta2=FLAGS.adam_beta2,
    epsilon=FLAGS.opt_epsilon)
elif FLAGS.optimizer == 'ftrl':
  optimizer = tf.train.FtrlOptimizer(
    learning_rate,
    learning_rate_power=FLAGS.ftrl_learning_rate_power,
    initial_accumulator_value=FLAGS.ftrl_initial_accumulator_value,
    l1_regularization_strength=FLAGS.ftrl_l1,
    l2_regularization_strength=FLAGS.ftrl_l2)
elif FLAGS.optimizer == 'momentum':
  optimizer = tf.train.MomentumOptimizer(
    learning_rate,
    momentum=FLAGS.momentum,
    name='Momentum')
elif FLAGS.optimizer == 'rmsprop':
  optimizer = tf.train.RMSPropOptimizer(
    learning_rate,
    decay=FLAGS.rmsprop_decay,
    momentum=FLAGS.rmsprop_momentum,
    epsilon=FLAGS.opt_epsilon)
elif FLAGS.optimizer == 'sgd':
  optimizer = tf.train.GradientDescentOptimizer(learning_rate)
else:
  raise ValueError('Optimizer [%s] was not recognized', FLAGS.optimizer)
return optimizer