python - 使用 seq2seq tensorflow 插件放置更多 RNN 层

Question

当我尝试使用 tensorflow 的 seq2seq 插件进行语言翻译的 seq2seq 模型时遇到问题。我正在关注本教程：https ://www.tensorflow.org/addons/tutorials/networks_seq2seq_nmt ，它展示了如何创建一个只有一个 RNN 层（在本例中为 LSTM）的解码器，但我不知道如何用这个插件放置不止一层，以及如何初始化它们。

下面是解码器类的代码：

  def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz, attention_type='luong'):
    super(Decoder, self).__init__()
    self.batch_sz = batch_sz
    self.dec_units = dec_units
    self.attention_type = attention_type
    
    # Embedding Layer
    self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
    
    #Final Dense layer on which softmax will be applied
    self.fc = tf.keras.layers.Dense(vocab_size)

    # Define the fundamental cell for decoder recurrent structure
    self.decoder_rnn_cell = tf.keras.layers.LSTMCell(self.dec_units)
   


    # Sampler
    self.sampler = tfa.seq2seq.sampler.TrainingSampler()

    # Create attention mechanism with memory = None
    self.attention_mechanism = self.build_attention_mechanism(self.dec_units, 
                                                              None, self.batch_sz*[max_length_input], self.attention_type)

    # Wrap attention mechanism with the fundamental rnn cell of decoder
    self.rnn_cell = self.build_rnn_cell(batch_sz)

    # Define the decoder with respect to fundamental rnn cell
    self.decoder = tfa.seq2seq.BasicDecoder(self.rnn_cell, sampler=self.sampler, output_layer=self.fc)

    
  def build_rnn_cell(self, batch_sz):
    rnn_cell = tfa.seq2seq.AttentionWrapper(self.decoder_rnn_cell, 
                                  self.attention_mechanism, attention_layer_size=self.dec_units)
    return rnn_cell

  def build_attention_mechanism(self, dec_units, memory, memory_sequence_length, attention_type='luong'):
    # ------------- #
    # typ: Which sort of attention (Bahdanau, Luong)
    # dec_units: final dimension of attention outputs 
    # memory: encoder hidden states of shape (batch_size, max_length_input, enc_units)
    # memory_sequence_length: 1d array of shape (batch_size) with every element set to max_length_input (for masking purpose)

    if(attention_type=='bahdanau'):
      return tfa.seq2seq.BahdanauAttention(units=dec_units, memory=memory, memory_sequence_length=memory_sequence_length)
    else:
      return tfa.seq2seq.LuongAttention(units=dec_units, memory=memory, memory_sequence_length=memory_sequence_length)

  def build_initial_state(self, batch_sz, encoder_state, Dtype):
    decoder_initial_state = self.rnn_cell.get_initial_state(batch_size=batch_sz, dtype=Dtype)
    decoder_initial_state = decoder_initial_state.clone(cell_state=encoder_state)
    return decoder_initial_state


  def call(self, inputs, initial_state):
    x = self.embedding(inputs)
    outputs, _, _ = self.decoder(x, initial_state=initial_state, sequence_length=self.batch_sz*[max_length_output-1])
    return outputs

标签： pythontensorflowadd-onseq2seqencoder-decoder