Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Output
tensor(1.7188)
And what if we want to simply ignore data points with label (y=2)?
loss_fn = nn.NLLLoss(ignore_index=2)
loss_fn(dummy_log_probs, dummy_labels)
Output
tensor(1.5599)
And, once again, there is yet another loss function available for multiclass
classification. And, once again, it is very important to know when to use one or the
other, so you don’t end up with an inconsistent combination of model and loss
function.
Cross-Entropy Loss
The former loss function took log probabilities as an argument (together with the
labels, obviously). Guess what this function takes? Logits, of course! This is the
multiclass version of nn.BCEWithLogitsLoss().
"What does it mean, in practical terms?"
It means you should NOT add a logsoftmax as the last layer of your model when
using this loss function. This loss function combines both the logsoftmax layer and
the negative log-likelihood loss into one.
382 | Chapter 5: Convolutions