对与RNN的两种不同写法

对与RNN的两种不同写法：    

    def add_cell(self):
        lstm_cell = tf.contrib.rnn.BasicLSTMCell(self.cell_size, forget_bias=1.0,                 
                       state_is_tuple=True)
        with tf.name_scope('initial_state'):#变量名
            #初始化cell
            self.cell_init_state = lstm_cell.zero_state(self.batch_size, dtype=tf.float32)
        #定义dynamic_rnn
        self.cell_outputs, self.cell_final_state = tf.nn.dynamic_rnn(
            lstm_cell, self.l_in_y, initial_state=self.cell_init_state, time_major=False)



# 训练 LSTMRNN
if __name__ == '__main__':
    # 搭建 LSTMRNN 模型
    model = LSTMRNN(TIME_STEPS, INPUT_SIZE, OUTPUT_SIZE, CELL_SIZE, BATCH_SIZE)
    sess = tf.Session()
    merged = tf.summary.merge_all()
    writer = tf.summary.FileWriter("logs", sess.graph)
    sess.run(tf.global_variables_initializer())
 
    # relocate to the local dir and run this line to view it on Chrome (http://0.0.0.0:6006/):
    # $ tensorboard --logdir='logs'
 
    # matplotlib可视化
    plt.ion()  # 设置连续 plot
    plt.show()
 
    # 训练 200 次
    for i in range(200):
        seq, res, xs = get_batch()  # 提取 batch data

        #用户已if的方式控制model.cell_init_state: state
        if i == 0:
            # 初始化 data
            feed_dict = {
                model.xs: seq,
                model.ys: res,
            }
        else:
            feed_dict = {
                model.xs: seq,
                model.ys: res,
                model.cell_init_state: state  # 保持 state 的连续性
            }
 
        # 训练
        _, cost, state, pred = sess.run(
            [model.train_op, model.cost, model.cell_final_state, model.pred],
            feed_dict=feed_dict)
 
        # plotting
        '''plt.plot(xs[0, :], res[0].flatten(), 'r', xs[0, :], pred.flatten()[:TIME_STEPS], 'b--')
        plt.ylim((-1.2, 1.2))
        plt.draw()
        plt.pause(0.3)  # 每 0.3 s 刷新一次'''
 
        # 打印 cost 结果
        if i % 20 == 0:
            print('cost: ', round(cost, 4))
            result = sess.run(merged, feed_dict)
            writer.add_summary(result, i)

第二种：

#定义使用lstm结构为循环体结构且使用dropout的深层循环神经网络
        dropout_keep_prob = lstm_keep_prob if is_training else 1.0
        lstm_cells=[
                tf.nn.rnn_cell.DropoutWrapper(
                        tf.nn.rnn_cell.BasicLSTMCell(hidden_size),
                        output_keep_prob = dropout_keep_prob)
                for _ in range(num_layers)
                ]
        cell = tf.nn.rnn_cell.MultiRNNCell(lstm_cells)

outputs = []
        state = self.initial_state
        with tf.variable_scope('RNN'):
            for time_step in range(num_steps):
                #用if语句控制：
                if time_step >0 : 
                    tf.get_variable_scope().reuse_variables()
                #这里是每一个时间步（time_step）让state=state
                cell_output,state=cell(inputs[:,time_step,:],state)
                outputs.append(cell_output)


def run_epoch(session,model,batches,train_op,output_log,step):
        #计算perplxity的辅助变量。
        total_costs=0.0
        iters=0
        state =session.run(model.initial_state)
        # 训练一个epoch
        for x,y in batches:
            #在当前batch上运行train_op并计算损失值。交叉熵损失函数（loss）计算的就是下一个单词                    
              为给定单词的概率：
            cost,state,_ = session.run_epoch(
                    [model.cost,model.final_state,train_op],
                    {model.input_data:x,
                     model.targets:y,
                     #这里是每一个epoch，让state=上一步的final_state   
                     model.initial_state:state}
                    )
            total_costs += cost  #总的损失
            iters += model.num_steps #迭代次数
            
            
            #只有在训练时输出日志
            if output_log and step%100==0:
                print ("after %d steps , perplexit is %.3f" %            
                              (step,np.exp(total_costs/iters)))
            step +=1
        #返回给指定model在给定数据上的perplexity的值
        return step , np.exp(total_costs/iters) #e的多少次方