Per-class and per-sample weighting

Thanks. I used above code and it got error in backward()

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-22-88aafe38e0f1> in <module>()
     15 loss =(loss * sample_weight / sample_weight.sum()).sum()
     16 print (sample_weight.shape, loss.shape)
---> 17 loss.mean().backward()
     18 
     19 #loss_total = torch.mean(loss * weights)

1 frames
/usr/local/lib/python3.6/dist-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)
     91     Variable._execution_engine.run_backward(
     92         tensors, grad_tensors, retain_graph, create_graph,
---> 93         allow_unreachable=True)  # allow_unreachable flag
     94 
     95 

RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn