How to modify the resnet 50 in Pytorch (pre-trained) to give Multiple Outputs for Multi-Label Classification
A softmax layer helps:

class ResNet(nn.Module):
def init(self):
    super(ResNet, self).init()
    resnet = models.resnet50(pretrained=True)
    modules = list(resnet.children())[:-1]
    self.resnet = nn.Sequential(*modules)
    self.fc=nn.Linear(2048,10),

def forward(self, x):

x1 = torch.softmax(self.resnet(x), dim=-1)
#x1=self.resnet(x)
#x1 = x1.view(x1.size(0), -1)  
#x1=self.fc(x1)

x2 = torch.softmax(self.resnet(x) dim=-1)
#x2= x2.view(x2.size(0), -1)
#x2=self.fc(x2)

return x1,x2


To know more about the dim attribute, corss reference here: https://stackoverflow.com/questions/52513802/pytorch-softmax-with-dim

I hope this what you were looking for.