copynet.py 文件源码

def _add_train_op(self):
    """Sets self._train_op, op to run for training."""
    config = self._config

    tvars = tf.trainable_variables()
    with tf.device(self._get_gpu(self._num_gpus - 1)):
      grads, global_norm = tf.clip_by_global_norm(
          tf.gradients(self._loss, tvars), config.max_grad_norm)
    tf.summary.scalar('global_norm', global_norm)

    lr_rate = tf.maximum(
        config.min_lr,  # min_lr_rate.
        tf.train.exponential_decay(config.lr, self.global_step,
                                   config.decay_steps, config.decay_rate))

    optimizer = tf.train.AdamOptimizer(lr_rate, epsilon=config.adam_epsilon)

    tf.summary.scalar('learning_rate', lr_rate)
    self._train_op = optimizer.apply_gradients(
        zip(grads, tvars), global_step=self.global_step, name='train_step')