使用RNN生成一系列张量

Question

我正在构建一个rnn，我使用tf.nn.dynamic_rnn来产生输出和状态。代码如下（tf版本1.3）：

import tensorflow as tf    
def lstm_cell():
        return tf.contrib.rnn.DropoutWrapper(tf.contrib.rnn.BasicLSTMCell(128), output_keep_prob=0.7)

cell= tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(3)])

initial_state= cell.zero_state(1, tf.float32)

layer = tf.placeholder(tf.float32, [1,1,36])

outputs, state=tf.nn.dynamic_rnn(cell=cell, inputs=layer, initial_state=initial_state)

由于输入张量始终为批量大小= 1，因此initial_state和state也具有批量大小1.层也是batch_size = 1的输入，并且对于每个单元，有36个节点（嵌入序列的大小）。每层都有lstm_size 128。

当我循环rnn单元格时出现问题。

rnn_outputs_sequence=outputs
for i in range(1, num_pics, 1):
    outputs, state=tf.nn.dynamic_rnn(cell=cell, inputs=outputs, initial_state=state)
    rnn_outputs_sequence=tf.concat((rnn_outputs_sequence, outputs),axis=1)

rnn_outputs_sequence预计有形状[1，num_pics，36]。但是，这会触发错误：

Trying to share variable rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel, but specified shape (256, 512) and found shape (164, 512).

我无法弄清楚这种形状[164,512]。任何人都可以帮我解决这个问题吗？谢谢。

Answer 1

import tensorflow as tf
def lstm_cell():
    return tf.contrib.rnn.DropoutWrapper(tf.contrib.rnn.BasicLSTMCell(128), output_keep_prob=0.7)

cell= tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(2)])

initial_state= cell.zero_state(1, tf.float32)

layer = tf.placeholder(tf.float32, [1,1,36])

outputs, state=tf.nn.dynamic_rnn(cell=cell, inputs=layer, initial_state=initial_state)

outputs = tf.reshape(outputs, shape=[1, -1])
outputs = tf.layers.dense(outputs, 36,\
                                 kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=False))
outputs = tf.reshape(outputs, shape=[1, 1, -1])
rnn_outputs_sequence=outputs
print(outputs)
for i in range(1, 16, 1):
    outputs, state=tf.nn.dynamic_rnn(cell=cell, inputs=outputs, initial_state=state)
    outputs = tf.reshape(outputs, shape=[1, -1])
    outputs = tf.layers.dense(outputs, 36,\
                                 kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=False))
    outputs = tf.reshape(outputs, shape=[1, 1, -1])
    print(outputs)
    rnn_outputs_sequence=tf.concat((rnn_outputs_sequence, outputs),axis=1)
print(rnn_outputs_sequence)