optimizer.py 文件源码

def __init__(self, opt, average_decay=0.9999, num_updates=None,
                 sequential_update=True):
        """Construct a new MovingAverageOptimizer.

        Args:
          opt: A tf.Optimizer that will be used to compute and apply gradients.
          average_decay: Float.  Decay to use to maintain the moving averages
                         of trained variables.
                         See tf.train.ExponentialMovingAverage for details.
          num_updates: Optional count of number of updates applied to variables.
                       See tf.train.ExponentialMovingAverage for details.
          sequential_update: Bool. If False, will compute the moving average at the
                             same time as the model is updated, potentially doing
                             benign data races.
                             If True, will update the moving average after gradient
                             updates.
        """
        self._optimizer = opt
        self._ema = tf.train.ExponentialMovingAverage(
            average_decay, num_updates=num_updates)
        self._variable_map = None
        self._sequential_update = sequential_update