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

logit1 = log_odds_ratio(.9)
logit2 = log_odds_ratio(.2)
dummy_labels = torch.tensor([1.0, 0.0])
dummy_logits = torch.tensor([logit1, logit2])
print(dummy_logits)
Output
tensor([ 2.1972, -1.3863])
We have logits, and we have labels. Time to compute the loss:
loss = loss_fn_logits(dummy_logits, dummy_labels)
loss
Output
tensor(0.1643)
OK, we got the same result, as expected.
Imbalanced Dataset
In our dummy example with two data points, we had one of each class: positive and
negative. The dataset was perfectly balanced. Let’s create another dummy example
but with an imbalance, adding two extra data points belonging to the negative
class. For the sake of simplicity and to illustrate a quirk in the behavior of
nn.BCEWithLogitsLoss(), I will give those two extra points the same logits as the
other data point in the negative class. It looks like this:
dummy_imb_labels = torch.tensor([1.0, 0.0, 0.0, 0.0])
dummy_imb_logits = torch.tensor([logit1, logit2, logit2, logit2])
Clearly, this is an imbalanced dataset. There are three times more data points in
the negative class than in the positive one. Now, let’s turn to the pos_weight
228 | Chapter 3: A Simple Classification Problem