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

To make it clear: In this chapter, we’re dealing with a single-label
binary classification (we have only one label per data point), and
the label is binary (there are only two possible values for it, zero
or one). If the label is zero, we say it belongs to the negative class.
If the label is one, it belongs to the positive class.
Please do not confuse the positive and negative classes of our
single label with c, the so-called class number in the
documentation. That c corresponds to the number of different
labels associated with a data point. In our example, c = 1.
You can use this argument to handle imbalanced datasets, but
there’s more to it than meets the eye. We’ll get back to it in the
next sub-section.
Enough talking (or writing!): Let’s see how to use this loss in code. We start by
creating the loss function itself:
loss_fn_logits = nn.BCEWithLogitsLoss(reduction='mean')
loss_fn_logits
Output
BCEWithLogitsLoss()
Next, we use logits and labels to compute the loss. Following the same principle as
before, logits first, then labels. To keep the example consistent, let’s get the values
of the logits corresponding to the probabilities we used before, 0.9 and 0.2, using
our log_odds_ratio() function:
Loss | 227