Input dimension for CrossEntropy Loss in PyTorch
For a binary classification problem with batch_size = 1, I have logit and label values using which I need to calculate loss.

logit: tensor([0.1198, 0.1911], device='cuda:0', grad_fn=&lt;AddBackward0&gt;)
label: tensor(1], device='cuda:0')
# calculate loss
loss_criterion = nn.CrossEntropyLoss()
loss_criterion.cuda()
loss = loss_criterion( b_logits, b_labels )


However, this always results in the following error,

IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)


What input dimensions is the CrossEntropyLoss actually asking for?

You are passing wrong shape of tensors.
shape should be (from doc)


  
  Input: (N,C) where C = number of classes
  Target: (N) where each value is 0 ≤ targets[i] ≤ C−1
  


So here, b_logits shape should be ([1,2]) instead of ([2]) to make it right shape you can use torch.view like b_logits.view(1,-1).  

And b_labels shape should be ([1]).
Ex.:

b_logits = torch.tensor([0.1198, 0.1911], requires_grad=True)
b_labels = torch.tensor([1])
loss_criterion = nn.CrossEntropyLoss()

loss = loss_criterion( b_logits.view(1,-1), b_labels )
loss
tensor(0.6581, grad_fn=&lt;NllLossBackward&gt;)

