Weighting the cross-entropy loss function for binary classification

Hi everyone. I am dealing with the Breast Histopathology Images dataset from Kaggle. The class distribution is:

  • 198,738 negative examples (i.e., no breast cancer)
  • 78,786 positive examples (i.e., indicating breast cancer was found in the patch)

I am defining the loss function: (as referred from here)

# Assign the class weights and pop it to GPU
from torch import nn

weights = [0.4, 1]

learn = cnn_learner(data, models.resnet50, metrics=[accuracy]).to_fp16()
learn.loss_func = nn.CrossEntropyLoss(weight=class_weights)

Is the right way or is there anything better than this approach for this case? Thank you in advance.


[Changing default loss functions](http://this post) answers the question i believe. Also fastai loss function are indeed torch objects.

When doing my research it was the first result, hope it will help future visitor too