sentiment_rnn.py 文件源码

def get_optimizer(self, learning_rate = 0.001, grad_clip = 5):
        #-----------------------------------------------------------------------
        # Build a loss function
        #-----------------------------------------------------------------------
        with tf.variable_scope('loss'):
            loss = tf.losses.mean_squared_error(self.target, self.output)

        #-----------------------------------------------------------------------
        # Build the optimizer
        #-----------------------------------------------------------------------
        with tf.variable_scope('optimizer'):
            tvars     = tf.trainable_variables()
            grads, _  = tf.clip_by_global_norm(tf.gradients(loss, tvars),
                                               grad_clip)
            train_op  = tf.train.AdamOptimizer(learning_rate)
            optimizer = train_op.apply_gradients(zip(grads, tvars))

        return optimizer, loss