tf.contrib.rnn.static_rnn与tf.nn.dynamic_rnn区别

Posted 2021-01-01 djangoblog

tf.contrib.rnn.static_rnn与tf.nn.dynamic_rnn区别

https://blog.csdn.net/u014365862/article/details/78238807

MachineLP的Github（欢迎follow）：https://github.com/MachineLP

我的GitHub：https://github.com/MachineLP/train_cnn-rnn-attention 自己搭建的一个框架，包含模型有：vgg(vgg16,vgg19), resnet(resnet_v2_50,resnet_v2_101,resnet_v2_152), inception_v4, inception_resnet_v2等。

 

1.  
   chunk_size = 256
2.  
   chunk_n = 160
3.  
   rnn_size = 256
4.  
   num_layers = 2
5.  
   n_output_layer = MAX_CAPTCHA*CHAR_SET_LEN # 输出层

 

单层rnn：

 

tf.contrib.rnn.static_rnn：

输入：[步长,batch,input] 

输出：[n_steps,batch,n_hidden] 

还有rnn中加dropout

 

 

1.  
   def recurrent_neural_network(data):
2.  
    
3.  
   data = tf.reshape(data, [-1, chunk_n, chunk_size])
4.  
   data = tf.transpose(data, [1,0,2])
5.  
   data = tf.reshape(data, [-1, chunk_size])
6.  
   data = tf.split(data,chunk_n)
7.  
    
8.  
   # 只用RNN
9.  
   layer = {‘w_‘:tf.Variable(tf.random_normal([rnn_size, n_output_layer])), ‘b_‘:tf.Variable(tf.random_normal([n_output_layer]))}
10.  
    lstm_cell = tf.contrib.rnn.BasicLSTMCell(rnn_size)
11.  
    outputs, status = tf.contrib.rnn.static_rnn(lstm_cell, data, dtype=tf.float32)
12.  
    # outputs = tf.transpose(outputs, [1,0,2])
13.  
    # outputs = tf.reshape(outputs, [-1, chunk_n*rnn_size])
14.  
    ouput = tf.add(tf.matmul(outputs[-1], layer[‘w_‘]), layer[‘b_‘])
15.  
     
16.  
    return ouput

 

 

 

多层rnn：

 

tf.nn.dynamic_rnn：

输入：[batch,步长,input] 
输出：[batch,n_steps,n_hidden] 
所以我们需要tf.transpose(outputs, [1, 0, 2])，这样就可以取到最后一步的output

 

1.  
   def recurrent_neural_network(data):
2.  
   # [batch,chunk_n,input]
3.  
   data = tf.reshape(data, [-1, chunk_n, chunk_size])
4.  
   #data = tf.transpose(data, [1,0,2])
5.  
   #data = tf.reshape(data, [-1, chunk_size])
6.  
   #data = tf.split(data,chunk_n)
7.  
    
8.  
   # 只用RNN
9.  
   layer = {‘w_‘:tf.Variable(tf.random_normal([rnn_size, n_output_layer])), ‘b_‘:tf.Variable(tf.random_normal([n_output_layer]))}
10.  
    #1
11.  
    # lstm_cell1 = tf.contrib.rnn.BasicLSTMCell(rnn_size)
12.  
    # outputs1, status1 = tf.contrib.rnn.static_rnn(lstm_cell1, data, dtype=tf.float32)
13.  
     
14.  
    def lstm_cell():
15.  
    return tf.contrib.rnn.LSTMCell(rnn_size)
16.  
    def attn_cell():
17.  
    return tf.contrib.rnn.DropoutWrapper(lstm_cell(), output_keep_prob=keep_prob)
18.  
    # stack = tf.contrib.rnn.MultiRNNCell([attn_cell() for _ in range(0, num_layers)], state_is_tuple=True)
19.  
    stack = tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(0, num_layers)], state_is_tuple=True)
20.  
    # outputs, _ = tf.nn.dynamic_rnn(stack, data, seq_len, dtype=tf.float32)
21.  
    outputs, _ = tf.nn.dynamic_rnn(stack, data, dtype=tf.float32)
22.  
    # [batch,chunk_n,rnn_size] -> [chunk_n,batch,rnn_size]
23.  
    outputs = tf.transpose(outputs, (1, 0, 2))
24.  
     
25.  
    ouput = tf.add(tf.matmul(outputs[-1], layer[‘w_‘]), layer[‘b_‘])
26.  
     
27.  
    return ouput