optimizer.py 文件源码

def apply_gradients(self, grads_and_vars, global_step=None):
        """Apply gradients to model variables specified in `grads_and_vars`.

        `apply_gradients` returns an op that calls
        `tf.train.Optimizer.apply_gradients` and then zeros the gradient
        variables stored in `self.grads_and_vars`.

        Args:
            grads_and_vars (list): Description.
            global_step (None, optional): tensorflow global_step variable.

        Returns:
            (tf.Operation): Applies gradient update to model followed by an
                internal gradient zeroing operation to `self.grads_and_vars`.

        """
        self.mini_flag = tf.assign(self.mini_flag, tf.constant([0], dtype = tf.float32))
        # grads_and_vars = self.aggregate_gradients(grads_and_vars, method='average')
        with tf.control_dependencies([self.mini_flag]):
            optimize = self._optimizer.apply_gradients(grads_and_vars,
                                                       global_step=global_step)
        #return [optimize, self.zero_grad()]
        return optimize