Thank you for replying. I just want to set positive label as (1,0) and negative label as (0,1).
If I have 1 positive sample and 10 negative samples, how can I use pos_weight  to make the loss derived from positive sample 10 times greater than negative ones?
In the example, preds_pos_wrong and label_pos are the logits for just one case.
For (0.5, 1.5) in preds_pos_wrong, 1.5 is the score for the positive class and 0.5 is the score for the negative class, and its label are (1, 0), which means the possibility of positive class are 1 and the possibility of negative class are 0.
Have I got that right for the usage of preds_pos_wrong or I’m wrong?