先说结论：
原来，他们与维度无关，
只要维度合适，都能计算。哈哈哈哈
测试代码：

    model = Net(10,128,Config.num_output_feat)inputs = torch.randn(3,5,10)res = model(inputs)# 3X5print(res)label = torch.randn(3,5)# loss_fn mseloss的事搞定了，哈哈哈哈loss_fn = nn.MSELoss()print(loss_fn(res,label))a = torch.Tensor(1)print("aaaa===",a)print("bbbb===",loss_fn(a,a))print("cccc====",torch.Tensor([1,2]))print("dddd=====",loss_fn(torch.Tensor([1,2]),torch.Tensor([1,2])))### 下面验证 分类损失的事loss_fn = nn.CrossEntropyLoss()# 3 X 4inputs = torch.Tensor([[1,2,3,4],[4,3,2,1],[0,0,1,2]])labels = torch.Tensor([[0,1,0,0],[1,0,0,0],[1,0,0,0]])res1 = loss_fn(inputs,labels)res = torch.argmax(inputs,dim=1)label1 = torch.argmax(labels,dim=1)print("res1=",res1, "res=",res,"labels=",labels,"label1=",label1,(res==label1).sum()/3)

因此，对于回归问题而言，最好可以用卷积来取代线性层！！