Question

因此，我知道通常与Pytorch一起使用LSTM。但这使我感到烦恼，您只能在LSTM中为所有图层指定一个hidden_size。像这样：

lstm = nn.LSTM(input_size=26, hidden_size=128, num_layers=3, dropout=dropout_chance, batch_first=True)

因此，对于所有三层，大小将为128。但是，实际上没有办法说第一层应该是128，第二层应该是32，第三层应该是128吗？如果我错过了文档中的内容，或者您知道解决方法，请告诉我，谢谢！

Answer 1

实际上，这取决于输入的形状，您可以看到How to decide input and hidden layer dimension to torch.nn.RNN?。另外，您必须了解什么是输入和输出，因为有不同的方式来处理输入和输出。在A Beginner’s Guide on Recurrent Neural Networks with PyTorch中，您可以看到模型如何输入输入数据。您的模型可以是

lstm = nn.LSTM(input_size=26, hidden_size=128, num_layers=3, dropout=dropout_chance, batch_first=True)
lstm2 = nn.LSTM(input_size=26, hidden_size=32, num_layers=3, dropout=dropout_chance, batch_first=True)
lstm3 = nn.LSTM(input_size=26, hidden_size=128, num_layers=3, dropout=dropout_chance, batch_first=True)

有关多层，请参见此model。

# sequence classification model
class M1(nn.Module):
    def __init__(self):
        super(M1, self).__init__()
        
        self.recurrent_layer  = nn.LSTM(hidden_size = 100, input_size = 75, num_layers = 5)
        self.recurrent_layer1  = nn.LSTM(hidden_size = 200, input_size = 100, num_layers = 5)
        self.recurrent_layer2  = nn.LSTM(hidden_size = 300, input_size = 200, num_layers = 5)
        self.project_layer     = nn.Linear(300, 200)
        self.project_layer1    = nn.Linear(200, 100)
        self.project_layer2    = nn.Linear(100, 10)
    
    # the size of input is [batch_size, seq_len(15), input_dim(75)]
    # the size of logits is [batch_size, num_class]
    def forward(self, input, h_t_1=None, c_t_1=None):
        # the size of rnn_outputs is [batch_size, seq_len, rnn_size]
        # self.recurrent_layer.flatten_parameters()
        rnn_outputs, (hn, cn) = self.recurrent_layer(input)
        rnn_outputs, (hn, cn) = self.recurrent_layer1(rnn_outputs)
        rnn_outputs, (hn, cn) = self.recurrent_layer2(rnn_outputs)
        # classify the last step of rnn_outpus
        # the size of logits is [batch_size, num_class]
        logits = self.project_layer(rnn_outputs[:,-1])
        logits = self.project_layer1(logits)
        logits = self.project_layer2(logits)
        return logits

如何在Pytorch LSTM / GRU / RNN中指定不同的图层大小

1 个答案: