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。