class LSTM(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim, num_layers): super(LSTM, self).__init__() self.hidden_dim = hidden_dim self.num_layers = num_layers self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True) self.fc = nn.Linear(hidden_dim, output_dim) def forward(self, x): h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).to(device) c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).to(device) out, _ = self.lstm(x, (h0, c0)) out = self.fc(out) return out[:, -1, :]为我的这段代码添加一个dropout层

可以在LSTM类的__init__方法中添加一个nn.Dropout层，如下所示：

class LSTM(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
super(LSTM, self).__init__()
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
self.dropout = nn.Dropout(p=.5) # 添加dropout层
self.fc = nn.Linear(hidden_dim, output_dim)

def forward(self, x):
h = torch.zeros(self.num_layers, x.size(), self.hidden_dim).to(device)
c = torch.zeros(self.num_layers, x.size(), self.hidden_dim).to(device)
out, _ = self.lstm(x, (h, c))
out = self.dropout(out) # 在LSTM输出后添加dropout层
out = self.fc(out)
return out[:, -1, :]