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] class_weights=torch.FloatTensor(weights).cuda() 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.