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

Output
tensor([[-1.5229, -0.3146, -2.9600],
[-1.7934, -1.0044, -0.7607],
[-1.2513, -1.0136, -1.0471],
[-2.6799, -0.2219, -2.0367],
[-1.0728, -1.9098, -0.6737]])
Can you hand-pick the log probabilities that are going to be
actually used in the loss computation?
relevant_log_probs = torch.tensor([-1.5229, -1.7934, -1.0136,
-2.0367, -1.9098])
-relevant_log_probs.mean()
Output
tensor(1.6553)
Now, let’s use nn.NLLLoss() to create the actual loss function, and then use
predictions and labels to check if we got the relevant log probabilities right:
loss_fn = nn.NLLLoss()
loss_fn(dummy_log_probs, dummy_labels)
Output
tensor(1.6553)
Right, indeed! What if we want to balance our dataset, giving data points with label
(y=2) double the weight of the other classes?
loss_fn = nn.NLLLoss(weight=torch.tensor([1., 1., 2.]))
loss_fn(dummy_log_probs, dummy_labels)
A Multiclass Classification Problem | 381