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

The nn.BCELoss() higher-order function takes two optional arguments (the others
are deprecated, and you can safely ignore them):
• reduction: It takes either mean, sum, or none. The default mean corresponds to
our Equation 3.15 above. As expected, sum will return the sum of the errors,
instead of the average. The last option, none, corresponds to the unreduced
form; that is, it returns the full array of errors.
• weight: The default is none, meaning every data point has equal weight. If
supplied, it needs to be a tensor with a size equal to the number of elements in a
mini-batch, representing the weights assigned to each element in the batch. In
other words, this argument allows you to assign different weights to each
element of the current batch, based on its position. So, the first element would
have a given weight, the second element would have a different weight, and so
on, regardless of the actual class of that particular data point. Sounds
confusing? Weird? Yes, this is weird; I think so too. Of course, this is not useless
or a mistake, but the proper usage of this argument is a more advanced topic
and outside the scope of this book.
This argument DOES NOT help with weighting imbalanced
datasets! We’ll see how to handle that shortly.
We’ll be sticking with the default arguments, corresponding to Equation 3.15
above.
loss_fn = nn.BCELoss(reduction='mean')
loss_fn
Output
BCELoss()
As expected, nn.BCELoss() returned another function; that is, the actual loss
function. The latter takes both predictions and labels to compute the loss.
224 | Chapter 3: A Simple Classification Problem