tacotron.py 文件源码

def add_train_op(self, loss):
        self.global_step = tf.Variable(0, name='global_step', trainable=False)

        opt = tf.train.AdamOptimizer(learning_rate=self.lr)

        gradients, variables = zip(*opt.compute_gradients(loss))
        # save selected gradient summaries
        #for grad in gradients:
            #if 'BasicDecoder' in grad.name or 'gru_cell' in grad.name or 'highway_3' in grad.name:
                #tf.summary.scalar(grad.name, tf.reduce_sum(grad))

        # optionally cap and noise gradients to regularize
        if self.config.cap_grads > 0:
            with tf.variable_scope('cap_grads'):
                tf.summary.scalar('global_gradient_norm', tf.global_norm(gradients))
                gradients, _ = tf.clip_by_global_norm(gradients, self.config.cap_grads)

        train_op = opt.apply_gradients(zip(gradients, variables), global_step=self.global_step)
        return train_op