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

argument of nn.BCEWithLogitsLoss(). To compensate for the imbalance, one can
set the weight to equal the ratio of negative to positive examples:
In our imbalanced dummy example, the result would be 3.0. This way, every point
in the positive class would have its corresponding loss multiplied by three. Since
there is a single label for each data point (c = 1), the tensor used as an argument for
pos_weight has only one element: tensor([3.0]). We could compute it like this:
n_neg = (dummy_imb_labels == 0).sum().float()
n_pos = (dummy_imb_labels == 1).sum().float()
pos_weight = (n_neg / n_pos).view(1,)
pos_weight
Output
tensor([3])
Now, let’s create yet another loss function, including the pos_weight argument this
time:
loss_fn_imb = nn.BCEWithLogitsLoss(
reduction='mean',
pos_weight=pos_weight
)
Then, we can use this weighted loss function to compute the loss for our
imbalanced dataset. I guess one would expect the same loss as before; after all,
this is a weighted loss. Right?
loss = loss_fn_imb(dummy_imb_logits, dummy_imb_labels)
loss
Loss | 229