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我有一个带有 LSTM 的类，然后是 nn.ModuleList，我在其中定义了另外两个 LSTM 层，但是循环的前向函数失败并出现错误“forward() 需要 1 个位置参数，但给出了 3 个”。

分享了下面的代码和错误。当我尝试传递较早的层输出、单元状态和隐藏状态值但 nn.ModuleList 不允许时，它似乎失败了。谁能帮我解决这个问题？

代码：

class RNN(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers, 
                 bidirectional, dropout, pad_idx,nadd_layers=2):
        
        super().__init__()

        self.addl_layer_list = [nn.ModuleList([nn.LSTM(hidden_dim*2,hidden_dim,num_layers=1,bidirectional=True,dropout=dropout), nn.LSTM(hidden_dim*2,hidden_dim,num_layers=1,bidirectional=True,dropout=dropout)])]
        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx = pad_idx)
        self.nlayers = nadd_layers

        # self.layers = nn.ModuleList([nn.Linear(in_f, out_f) for in_f, out_f in zip(sizes, sizes[1:])])

        # for layer in range(self.nlayers):
        #   layer
        self.rnn1 = nn.LSTM(embedding_dim, 
                           hidden_dim, 
                           num_layers=1, 
                           bidirectional=True, 
                           dropout=dropout)
        self.addl_layers = nn.ModuleList(self.addl_layer_list)
        self.fc = nn.Linear(hidden_dim, output_dim)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, text, text_lengths):

      #text = [sent len, batch size] https://stackoverflow.com/questions/49224413/difference-between-1-lstm-with-num-layers-2-and-2-lstms-in-pytorch
      embedded = self.dropout(self.embedding(text))
      #pack sequence
      packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, text_lengths)
      packed_output, (hidden, cell) = self.rnn1(packed_embedded)
      hidden_final = hidden
      hidden_set = True
      for layer in range(self.nlayers):
        packed_output1, (hidden1, cell1) = self.addl_layers[layer](packed_output,(hidden, cell))

错误：

TypeError                                 Traceback (most recent call last)
<ipython-input-164-4185e35f9156> in <module>()
      7     start_time = time.time()
      8 
----> 9     train_loss, train_acc = train(model, train_iterator, optimizer, criterion)
     10     valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)
     11 

3 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

TypeError: forward() takes 1 positional argument but 3 were given
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1 回答 1

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堆叠的 LSTM 在不同层之间共享隐藏状态。最后的循环应重写为：

_, (hidden, _) = self.rnn1(packed_embedded)
for layer in range(self.nlayers):
    _, (hidden1, _) = self.addl_layers[layer](hidden)
    hidden = hidden1
于 2020-11-18T20:05:51.313 回答