我正在制作一个seq2seq模型，该模型使用Bi-LSTM作为编码器和解码器中的Attention机制。对于单层LSTM模型来说，它工作正常。我的编码器看起来像这样。
编码器：
def encoding_layer(self, rnn_inputs, rnn_size, num_layers, keep_prob,
source_vocab_size,
encoding_embedding_size,
source_sequence_length,
emb_matrix):
embed = tf.nn.embedding_lookup(emb_matrix, rnn_inputs)
stacked_cells = tf.contrib.rnn.DropoutWrapper(tf.contrib.rnn.LSTMCell(rnn_size), keep_prob)
outputs, state = tf.nn.bidirectional_dynamic_rnn(cell_fw=stacked_cells,
cell_bw=stacked_cells,
inputs=embed,
sequence_length=source_sequence_length,
dtype=tf.float32)
concat_outputs = tf.concat(outputs, 2)
cell_state_fw, cell_state_bw = state
cell_state_final = tf.concat([cell_state_fw.c, cell_state_bw.c], 1)
hidden_state_final = tf.concat([cell_state_fw.h, cell_state_bw.h], 1)
encoder_final_state = tf.nn.rnn_cell.LSTMStateTuple(c=cell_state_final, h=hidden_state_final)
return concat_outputs, encoder_final_state
解码器：
def decoding_layer_train(self, encoder_outputs, encoder_state, dec_cell, dec_embed_input,
target_sequence_length, max_summary_length,
output_layer, keep_prob, rnn_size, batch_size):
rnn_size = 2 * rnn_size
dec_cell = tf.contrib.rnn.DropoutWrapper(tf.contrib.rnn.LSTMCell(rnn_size), keep_prob)
train_helper = tf.contrib.seq2seq.TrainingHelper(dec_embed_input, target_sequence_length)
attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(rnn_size, encoder_outputs,
memory_sequence_length=target_sequence_length)
attention_cell = tf.contrib.seq2seq.AttentionWrapper(dec_cell, attention_mechanism,
attention_layer_size=rnn_size/2)
state = attention_cell.zero_state(dtype=tf.float32, batch_size=batch_size)
state = state.clone(cell_state=encoder_state)
decoder = tf.contrib.seq2seq.BasicDecoder(cell=attention_cell, helper=train_helper,
initial_state=state,
output_layer=output_layer)
outputs, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder, impute_finished=True, maximum_iterations=max_summary_length)
return outputs
使用单层Bi-LSTM的上述配置，我的模型可以正常工作。但是，现在我想使用多层Bi-LSTM编码器和解码器。因此，在编码器和解码器中，如果我将单元格更改为：
stacked_cells = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.DropoutWrapper(tf.contrib.rnn.LSTMCell(rnn_size), keep_prob) for _ in range(num_layers)])
更改单元格后出现此错误：
AttributeError：“元组”对象没有属性“ c”
在这里， num_layers = 2
rnn_size = 128
embeddding_size = 50
因此，我想知道第二种情况下state到底返回了什么。以及如何将该状态传递给解码器。