seq2seq_attention_model.py 文件源码

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

    self._lr_rate = tf.maximum(
        hps.min_lr,  # min_lr_rate.
        tf.train.exponential_decay(hps.lr, self.global_step, 30000, 0.98))

    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), hps.max_grad_norm)
    tf.summary.scalar('global_norm', global_norm)
    optimizer = tf.train.GradientDescentOptimizer(self._lr_rate)
    tf.summary.scalar('learning rate', self._lr_rate)
    self._train_op = optimizer.apply_gradients(
        zip(grads, tvars), global_step=self.global_step, name='train_step')