qa_model.py 文件源码

def setup_train_op(self):
        """
        Add train_op to self
        """
        with tf.variable_scope("train_step"):
            adam_optimizer = tf.train.AdamOptimizer()
            grads, vars = zip(*adam_optimizer.compute_gradients(self.loss))

            clip_val = self.config.max_gradient_norm
            # if -1 then do not perform gradient clipping
            if clip_val != -1:
                clipped_grads, _ = tf.clip_by_global_norm(grads, self.config.max_gradient_norm)
                self.global_grad = tf.global_norm(clipped_grads)
                self.gradients = zip(clipped_grads, vars)
            else:
                self.global_grad = tf.global_norm(grads)
                self.gradients = zip(grads, vars)


            self.train_op = adam_optimizer.apply_gradients(self.gradients)

        self.init = tf.global_variables_initializer()