How to use Soft-label for Cross-Entropy loss?

3

HI There,

I want to use smooth labeling with the criterion=nn.CrossEntropyLoss() with batch size of 64. The labels are random number between 0.8 to 0.9 and the outputs are from sigmoid. The code is



label=(0.9-0.8)* torch.rand(b_size) + 0.8
    label=label.to(device).type(torch.LongTensor)

    # Forward pass real batch through D
    
    netD=netD.float()
    output = netD(real_cpu).view(-1)
    # Calculate loss on all-real batch
    output1=torch.zeros(64,64)
    for ii in range(64):
        output1[:,ii]=ii
    for ii in range(64):
        output1[ii,:]= output[ii].type(torch.LongTensor)
        
    errD_real = criterion(output1, label)

and the error is:
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

By applying (torch.LongTensor) all the labels and output become 0! and without (torch.LongTensor) it gave me error.