如何在Tensorflow中使用多层双向LSTM？

我想知道如何在Tensorflow中使用多层双向LSTM。如何在Tensorflow中使用多层双向LSTM？

我已经实现了双向LSTM的内容，但是我想将这个模型与添加了多层的模型进行比较。

我该如何在这部分添加一些代码？

x = tf.unstack(tf.transpose(x, perm=[1, 0, 2])) 
#print(x[0].get_shape()) 

# Define lstm cells with tensorflow 
# Forward direction cell 
lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) 
# Backward direction cell 
lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) 

# Get lstm cell output 
try: 
    outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, 
              dtype=tf.float32) 
except Exception: # Old TensorFlow version only returns outputs not states 
    outputs = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, 
            dtype=tf.float32) 

# Linear activation, using rnn inner loop last output 
outputs = tf.stack(outputs, axis=1) 
outputs = tf.reshape(outputs, (batch_size*n_steps, n_hidden*2)) 
outputs = tf.matmul(outputs, weights['out']) + biases['out'] 
outputs = tf.reshape(outputs, (batch_size, n_steps, n_classes))

来源

2017-09-13 Gi Yeon Shin

你可以用两种不同的方法适用于多层bilstm型号：

1）使用了以前的bilstm层作为输入到下bilstm。在开始时，您应该创建具有长度为num_layers的向前和向后单元格的阵列。并且

for n in range(num_layers): 
     cell_fw = cell_forw[n] 
     cell_bw = cell_back[n] 

     state_fw = cell_fw.zero_state(batch_size, tf.float32) 
     state_bw = cell_bw.zero_state(batch_size, tf.float32) 

     (output_fw, output_bw), last_state = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, output, 
                      initial_state_fw=state_fw, 
                      initial_state_bw=state_bw, 
                      scope='BLSTM_'+ str(n), 
                      dtype=tf.float32) 

     output = tf.concat([output_fw, output_bw], axis=2)

2）另外值得一看stacked bilstm。

来源

2017-10-09 14:02:35

我试过这个，得到这个错误：ValueError：变量bidirectional_rnn/fw/lstm_cell/kernel已经存在，不允许。你是否想在VarScope中设置reuse = True？你能提供一个有效的例子吗？ – Rahul

如何在Tensorflow中使用多层双向LSTM？

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