把多层双向RNN搞糊涂了

Question

我正在使用Tensorflow构建一个多层双向RNN，但是我对实现有点困惑。在

我构建了两个函数来创建多层双向RNN第一个很好，但是我不确定它的预测，因为它是单向多层RNN。以下是我的实现：

def encoding_layer_old(rnn_inputs, rnn_size, num_layers, keep_prob, 
                   source_sequence_length, source_vocab_size, 
                   encoding_embedding_size):
    """
    Create encoding layer
    :param rnn_inputs: Inputs for the RNN
    :param rnn_size: RNN Size
    :param num_layers: Number of layers
    :param keep_prob: Dropout keep probability
    :param source_sequence_length: a list of the lengths of each sequence in the batch
    :param source_vocab_size: vocabulary size of source data
    :param encoding_embedding_size: embedding size of source data
    :return: tuple (RNN output, RNN state)
    """
    # Encoder embedding
    enc_embed = tf.contrib.layers.embed_sequence(rnn_inputs, source_vocab_size, encoding_embedding_size)

    def create_cell_fw(rnn_size):
        with tf.variable_scope("create_cell_fw"):
            lstm_cell = tf.contrib.rnn.LSTMCell(rnn_size,initializer=tf.random_uniform_initializer(-0.1,0.1,seed=2), reuse=False)
            drop = tf.contrib.rnn.DropoutWrapper(lstm_cell, output_keep_prob=keep_prob)
        return drop
    def create_cell_bw(rnn_size):
        with tf.variable_scope("create_cell_bw"):
            lstm_cell = tf.contrib.rnn.LSTMCell(rnn_size,initializer=tf.random_uniform_initializer(-0.1,0.1,seed=2), reuse=False)
            drop = tf.contrib.rnn.DropoutWrapper(lstm_cell, output_keep_prob=keep_prob)
        return drop    


    enc_cell_fw = tf.contrib.rnn.MultiRNNCell([create_cell_fw(rnn_size) for _ in range(num_layers)])
    enc_cell_bw = tf.contrib.rnn.MultiRNNCell([create_cell_bw(rnn_size) for _ in range(num_layers)])
    ((encoder_fw_outputs, encoder_bw_outputs),(encoder_fw_final_state,encoder_bw_final_state)) = tf.nn.bidirectional_dynamic_rnn(enc_cell_fw,enc_cell_bw, enc_embed, 
                                                        sequence_length=source_sequence_length,dtype=tf.float32)
    encoder_outputs = tf.concat([encoder_fw_outputs, encoder_bw_outputs], 2)
    print(encoder_outputs)
    #encoder_final_state_c=[]#tf.Variable([num_layers] , dtype=tf.int32)
    #encoder_final_state_h=[]#tf.Variable([num_layers] , dtype=tf.int32)
    encoder_final_state = ()
    for x in range((num_layers)):
        encoder_final_state_c=tf.concat((encoder_fw_final_state[x].c, encoder_bw_final_state[x].c), 1)#tf.stack(tf.concat((encoder_fw_final_state[x].c, encoder_bw_final_state[x].c), 1))
        encoder_final_state_h=tf.concat((encoder_fw_final_state[x].h, encoder_bw_final_state[x].h), 1)# tf.stack(tf.concat((encoder_fw_final_state[x].h, encoder_bw_final_state[x].h), 1))
        encoder_final_state =encoder_final_state+ (tf.contrib.rnn.LSTMStateTuple(c=encoder_final_state_c,h=encoder_final_state_h),)

    #encoder_final_state = tf.contrib.rnn.LSTMStateTuple(c=encoder_final_state_c,h=encoder_final_state_h)
    print('before')
    print(encoder_fw_final_state)
    return encoder_outputs, encoder_final_state

我发现了另一个实现here，如下所示：

t

^{pr2}$

这个实现的问题是我得到了一个形状错误：

Trying to share variable bidirectional_rnn/fw/lstm_cell/kernel, but specified shape (168, 224) and found shape (256, 224).

似乎其他人在创建RNN单元时也遇到过类似的情况，解决方案是使用MultiRNcell创建分层单元。但是如果使用multirncell，我将无法使用第二个实现，因为multirncell不支持索引。因此，我不会在单元格列表中循环并创建多个RNN。在

我真的很感谢你的帮助来指导我。在

我用的是tensorflow 1.3

Answer 1

这两个代码看起来确实有点过于复杂。不管怎样，我尝试了一个简单得多的版本，它成功了。{{{cd2>中删除{cd2>后的代码。下面是我的简单实现。在

def encoding_layer(input_data, num_layers, rnn_size, sequence_length, keep_prob):

    output = input_data
    for layer in range(num_layers):
        with tf.variable_scope('encoder_{}'.format(layer),reuse=tf.AUTO_REUSE):

            cell_fw = tf.contrib.rnn.LSTMCell(rnn_size, initializer=tf.truncated_normal_initializer(-0.1, 0.1, seed=2))
            cell_fw = tf.contrib.rnn.DropoutWrapper(cell_fw, input_keep_prob = keep_prob)

            cell_bw = tf.contrib.rnn.LSTMCell(rnn_size, initializer=tf.truncated_normal_initializer(-0.1, 0.1, seed=2))
            cell_bw = tf.contrib.rnn.DropoutWrapper(cell_bw, input_keep_prob = keep_prob)

            outputs, states = tf.nn.bidirectional_dynamic_rnn(cell_fw, 
                                                              cell_bw, 
                                                              output,
                                                              sequence_length,
                                                              dtype=tf.float32)
            output = tf.concat(outputs,2)
            state = tf.concat(states,2)

    return output, state