我想通过提供一个图像作为输入来预测cnn模型的连续值作为输出： 下面是代码

class MultiLabelNN(nn.Module):
    def __init__(self):
        super(MultiLabelNN,self).__init__()
        self.conv1 = nn.Conv2d(3,64,5)
        self.pool = nn.MaxPool2d(2,2)
        self.conv2 = nn.Conv2d(64,128,5)        
        self.conv3 = nn.Conv2d(128,256,5)
        self.conv4 = nn.Conv2d(256,320,5)
        self.fc1 = nn.Linear(250880,2048)
        self.fc2 = nn.Linear(2048,1024)
        self.fc3 = nn.Linear(1024,512)        
        self.fc4 = nn.Linear(512,6)
    def forward(self,x):
        x = self.conv1(x)
        x = nn.ReLU(x)    
        x = self.pool(x)   
        x = self.conv2(x)
        x = nn.ReLU(x)
        x = self.pool(x)
        x = self.conv3(x)
        x = nn.ReLU(x)
        x = self.pool(x) 
        x = self.conv4(x)
        x = nn.ReLU(x)
        x = self.pool(x)        
        x = x.view(-1,250880)
        x = self.fc1(x)        
        x = self.fc2(x)       
        x = self.fc3(x)
        x = self.fc4(x)
        return x

我想在我的最后一个完全连接的层中支持向量回归，而不是线性回归（self.fc4 = nn.Linear（512，6））.....

我的问题是，有什么选择吗？

like self.fc4 = nn.SVR（512，6）

或我听说过的任何其他有关skorch（https://github.com/skorch-dev/skorch）

但是我无法使用它。请帮我。预先感谢。