Hi, I have labels in one-hot format with size [bsz, bsz*2]. My input also is a matrix of shape [bsz,bsz*2]. I want to use cross-entropy loss. I searched the pytorch doc and I found that we can’t apply cross-entropy loss on one hot except in the following way:

```
out = torch.FloatTensor([[0.05, 0.9, 0.05], [0.05, 0.05, 0.9], [0.9, 0.05, 0.05]])
y1 = torch.FloatTensor([[0, 1, 0], [0, 0, 1], [1, 0, 0]])
_, targets = y1.max(dim=0)
loss = nn.CrossEntropyLoss()(out, Variable(targets))
print(loss.item())
################################
a = torch.randn(3,3)
print(a.shape)
b = torch.randn(3,3)
c = torch.cat((a,b),dim=1)
lable = torch.randn(3,3)
_,target =lable.max(dim=0)
criterion = torch.nn.CrossEntropyLoss(reduction="sum")
loss = criterion(Variable(target),c)
IndexError: Dimension out of range (expected to be in the range of [-1, 0], but got 1)
```

I can’t understand why I get an error in the second case. Any help is really appreciated.