如何使用變量的最後一個狀態作爲Tensorflow中的下一個狀態？

Question

爲了學習的目的，我想在Tensorflow中建立自己的LSTM模型。問題是，如何訓練的方式是，某個時間步的狀態使用前一個時間步的狀態進行初始化。在Tensorflow中有這種機制嗎？

class Lstm: 

    def __init__(self, x, steps): 
     self.initial = tf.placeholder(tf.float32, [None, size]) 
     self.state = self.initial 
     for _ in range(steps): 
      x = self.layer_lstm(x, 100) 
     x = self.layer_softmax(x, 10) 
     self.prediction = x 

    def step_lstm(self, x, size): 
     stream = self.layer(x, size) 
     input_ = self.layer(x, size) 
     forget = self.layer(x, size, bias=1) 
     output = self.layer(x, size) 
     self.state = stream * input_ + self.state * forget 
     x = self.state * output 
     return x 

    def layer_softmax(self, x, size): 
     x = self.layer(x, size) 
     x = tf.nn.softmax(x) 
     return x 

    def layer(self, x, size, bias=0.1): 
     in_size = int(x.get_shape()[1]) 
     weight = tf.Variable(tf.truncated_normal([in_size, size], stddev=0.1)) 
     bias = tf.Variable(tf.constant(bias, shape=[size])) 
     x = tf.matmul(x, weight) + bias 
     return x 

Answer 1

@danijar - 你可能想看看「變量」的this page節如何保持在調用狀態到子一個簡單的例子。