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

It is not that hard, to be honest. Remember the reduction argument? If we set it to
sum, our loss function will only return the numerator of the equation above. And
then we can divide it by the weighted counts ourselves:
loss_fn_imb_sum = nn.BCEWithLogitsLoss(
reduction='sum',
pos_weight=pos_weight
)
loss = loss_fn_imb_sum(dummy_imb_logits, dummy_imb_labels)
loss = loss / (pos_weight * n_pos + n_neg)
loss
Output
tensor([0.1643])
There we go!
Model Configuration
In Chapter 2.1, we ended up with a lean "Model Configuration" section: We only
need to define a model, an appropriate loss function, and an optimizer. Let’s define
a model that produces logits and use nn.BCEWithLogitsLoss() as the loss function.
Since we have two features, and we are producing logits instead of probabilities,
our model will have one layer and one layer alone: Linear(2, 1). We will keep
using the SGD optimizer with a learning rate of 0.1 for now.
Model Configuration | 231