python - 堆叠两个具有不同隐藏层的 LSTM

Question

我想创建包含不同隐藏层的 LSTM 层来预测时间序列数据，因为 LSTM_1 的第一层包含 10 个隐藏层，LSTM_2 包含 1 个隐藏层，建议的神经网络架构如下所示

提出的神经网络架构

def __init__(self, nb_features=1, hidden_size_1=100, hidden_size_2=100, nb_layers_1 =10, nb_layers_2 = 1, dropout=0.5): #(self, nb_features=1, hidden_size=100, nb_layers=10, dropout=0.5):      
super(Sequence, self).__init__()
self.nb_features = nb_features
self.hidden_size_1 = hidden_size_1
self.hidden_size_2 = hidden_size_2
self.nb_layers_1 =nb_layers_1
self.nb_layers_2 = nb_layers_2
self.lstm_1 = nn.LSTM(self.nb_features, self.hidden_size_1, self.nb_layers_1, dropout=dropout)  
self.lstm_2 = nn.LSTM(self.hidden_size_1, self.hidden_size_2, self.nb_layers_2, dropout=dropout)
self.lin = nn.Linear(self.hidden_size_2, 1)

def forward(self, input):  
h_t1 = Variable(torch.zeros(self.nb_layers_1, input.size()[1], self.hidden_size_1))
c_t1 = Variable(torch.zeros(self.nb_layers_1, input.size()[1], self.hidden_size_1))
h_t2 = Variable(torch.zeros(self.nb_layers_2, input.size()[1], self.hidden_size_2))
c_t2 = Variable(torch.zeros(self.nb_layers_2, input.size()[1], self.hidden_size_2))
outputs = []

for i, input_t in enumerate(input.chunk(input.size(1))):
    h_t1, c_t1 = self.lstm_1(input_t, (h_t1, c_t1))
    h_t2, _ = self.lstm_2(h_t1, (h_t2, c_t2))
    output = self.lin(h_t2[-1])
    outputs += [output]

outputs = torch.stack(outputs, 1).squeeze(2)

return outputs

但是，上面的代码出现运行时错误“RuntimeError: Expected hidden[0] size (3, 60, 100), got (1, 60, 100)”。上面的代码是否正确，您能否提出一些建议？非常感谢

标签： pythonpytorchlstm