Getting Loss of Individual Points

I need to obtain the loss of individual points, but I would prefer to do so in batches of size greater than 1. I have attempted to do so using

loss = torch.nn.functional.nll_loss(output, label, reduction='none')

but I suspect this to be incorrect. Although I do get a feasible loss for each point, the process in which these losses are used is performing significantly worse than I’d expect, and I believe this to be due to this step being performed incorrectly.

Could you describe why using reduction='none' is wrong? It will return the loss for each sample of the batch without taking the default 'mean' reduction.

1 Like

It may well be correct, I’m just trying to locate the point of failure in my system, and I thought I may have been using the incorrect method, but if reduction='none' truly results in per-element losses, then the failure must be elsewhere.

You can verify it quickly by comparing the raw loss (with a mean reduction) against the already averaged loss as seen here:

batch_size = 64
nb_classes = 10

output = torch.randn(batch_size, nb_classes, requires_grad=True)
output = output.log_softmax(dim=1)
label = torch.randint(0, nb_classes, (batch_size,))

loss_raw = torch.nn.functional.nll_loss(output, label, reduction='none')
loss = torch.nn.functional.nll_loss(output, label)

# tensor(2.6052, grad_fn=<MeanBackward0>)

# #tensor(2.6052, grad_fn=<NllLossBackward0>)

and it should return the same value.