I am trying edge classification (binary), refering - 5.2 Edge Classification/Regression — DGL 0.6.1 documentation
The data has class imbalance. To handle that I tried modifying loss function by including weight option like this:
class_weights = [90.0] for e in range(epochs): .... loss = F.binary_cross_entropy_with_logits(logits[train_mask], edge_labels[train_mask], pos_weight=torch.FloatTensor(class_weights)) .... loss.backward() ....
But there is no importance given to those class weights. Any idea on why that could happen?