Question

Tensorflow版本1.0

我的问题是encoder_state参数对tf.contrib.seq2seq attention_decoder_fn_train期望的维度。

可以采用多层编码器状态输出吗？

上下文：

我想在 tensorflow 1.0 中创建多层双向注意力seq2seq 。

我的编码器：

cell = LSTM(n)
cell = MultiRnnCell([cell]*4)
((encoder_fw_outputs,encoder_bw_outputs),
 (encoder_fw_state,encoder_bw_state)) = (tf.nn.bidirectional_dynamic_rnn(cell_fw=cell, cell_bw = cell.... )

现在，多层双向编码器为每一层返回编码器cell_states[c]和hidden_states[h]，并且还用于向后和向前传递。我连接正向传递状态和反向传递状态以将其传递给encoder_state：

self.encoder_state = tf.concat((encoder_fw_state, encoder_bw_state), -1)

我把它传递给我的解码器：

decoder_fn_train = seq2seq.simple_decoder_fn_train(encoder_state=self.encoder_state)
(self.decoder_outputs_train,
 self.decoder_state_train,
 self.decoder_context_state_train) = seq2seq.dynamic_rnn_decoder(cell=decoder_cell,... )

但它会出现以下错误：

ValueError: The two structures don't have the same number of elements. First structure: Tensor("BidirectionalEncoder/transpose:0", shape=(?, 2, 2, 20), dtype=float32), second structure: (LSTMStateTuple(c=20, h=20), LSTMStateTuple(c=20, h=20)).

我的decoder_cell也是多层次的。

Link to my code

1：

Answer 1

我发现了我的实施问题。所以在这里发布。问题是w.r.t.连接encoder_fw_state和encoder_bw_state。正确的方法如下：

    self.encoder_state = []

    for i in range(self.num_layers):
        if isinstance(encoder_fw_state[i], LSTMStateTuple):

            encoder_state_c = tf.concat((encoder_fw_state[i].c, encoder_bw_state[i].c), 1, name='bidirectional_concat_c')
            encoder_state_h = tf.concat((encoder_fw_state[i].h, encoder_bw_state[i].h), 1, name='bidirectional_concat_h')
            encoder_state = LSTMStateTuple(c=encoder_state_c, h=encoder_state_h)
        elif isinstance(encoder_fw_state[i], tf.Tensor):
            encoder_state = tf.concat((encoder_fw_state[i], encoder_bw_state[i]), 1, name='bidirectional_concat')
        self.encoder_state.append(encoder_state)

    self.encoder_state = tuple(self.encoder_state)

多层编码器输出状态到Seq2Seq模型TF 1.0中的多层解码器

1 个答案: