Question

我正在使用tensorflow为文本求和实现seq2seq模型。对于编码器，我正在使用双向RNN层。编码层：

    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.MultiRNNCell([tf.contrib.rnn.DropoutWrapper(tf.contrib.rnn.LSTMCell(rnn_size), keep_prob) for _ in range(num_layers)])

    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)

    return concat_outputs, state[0]

对于解码器，我正在使用注意力机制。解码层：

    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):
    """
    Create a training process in decoding layer 
    :return: BasicDecoderOutput containing training logits and sample_id
    """

    dec_cell = tf.contrib.rnn.DropoutWrapper(dec_cell, 
                                             output_keep_prob=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

现在，BasicDecoder函数的初始状态期望状态为shape =（batch_size，rnn_size）。我的编码器输出shape =（batch_size，rnn_size）的两个状态（向前和向后）。

要使其正常工作，我仅使用编码器的一种状态（转发状态）。因此，我想知道同时使用编码层的向后编码和正向编码的方法。我应该同时添加前进和后退状态吗？

P.S。 -解码器不使用双向层。

Answer 1

如果您只想使用向后编码：

# Get only the last cell state of the backward cell
(_, _), (_, cell_state_bw) = tf.nn.bidirectional_dynamic_rnn(...)
# Pass the cell_state_bw as the initial state of the decoder cell
decoder = tf.contrib.seq2seq.BasicDecoder(..., initial_state=cell_state_bw, ...)

我建议您做什么：

# Get both last states
(_, _), (cell_state_fw, cell_state_bw) = tf.nn.bidirectional_dynamic_rnn(...)
# Concatenate the cell states together
cell_state_final = tf.concat([cell_state_fw.c, cell_state_bw.c], 1)
# Concatenate the hidden states together
hidden_state_final = tf.concat([cell_state_fw.h, cell_state_bw.h], 1)
# Create the actual final state
encoder_final_state = tf.nn.rnn_cell.LSTMStateTuple(c=cell_state_final, h=hidden_state_final)
# Now you can pass this as the initial state of the decoder

但是请注意，解码器单元的大小必须是编码器单元的大小的两倍，第二种方法才能起作用。

Answer 2

以前的回复中已经涵盖了大部分内容。

关于您的关注，“我应该同时添加正向和反向状态吗？”，据我所述，我们应该同时使用编码器的状态。否则，我们不会利用训练后的编码器状态。此外，“ bidirectional_dynamic_rnn”应具有两个不同的LSTM单元层：一个用于FW状态，另一个用于BW状态。

在seq2seq模型实现中需要建议

2 个答案: