base_optimizer.py 文件源码

def variables_to_restore(self, moving_avg_variables=None):
    """"""

    name_map = {}
    if moving_avg_variables is None:
      moving_avg_variables = tf.trainable_variables()
      moving_avg_variables += tf.moving_average_variables()
    # Remove duplicates
    moving_avg_variables = set(moving_avg_variables)
    # Collect all the variables with moving average,
    for v in moving_avg_variables:
      name_map[self.average_name(v)] = v
    # Make sure we restore variables without moving average as well.
    for v in list(set(tf.all_variables()) - moving_avg_variables):
      if v.op.name not in name_map:
        name_map[v.op.name] = v
    return name_map

  #===============================================================