如何在不影响反向传播的情况下改变损失函数的权重？

Question

我有一个带有零和整数的基本实量张量，我想通过在损失函数中将非零值乘以5来更改权重，但是这会影响反向传播并导致错误，还有其他方法吗通过“损失”功能实现这一目标？

def MSE(output,truth,batch_size):
   for i in range(batch_size):
    for pos,j in enumerate(truth[i]):
        if j != 0:
            output[i][pos] = output[i][pos]*5


   mse = ((truth.sub(output))**2).mean()
   return mse

下面的翻译部分

for epoch in range(EPOCH):
        losses = []
        for step,(im,pointlist) in enumerate(train_loader):

            im = im.to(device)
            output = cnn(im)
            y = torch.stack(pointlist,dim=1)
            y = y.type(torch.FloatTensor)
            y = y.to(device)
            loss = MSE(output,y,batch_size)
            optimizer.zero_grad()           
            loss.backward()                 # backpropagation, compute gradients
            optimizer.step()

            losses.append(loss.item())
        scheduler.step()
        print("Epoch={0},Loss={1:.6F}".format(epoch, np.average(np.array(losses))))

错误发生在loss.backward（）

    Traceback (most recent call last):
      File "CNN_Icarus.py", line 213, in <module>
        main()
    File "CNN_Icarus.py", line 203, in main
    loss.backward()# backpropagation, compute gradients
    File "C:\Users\et302\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\tensor.py", line 166, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)
    File "C:\Users\et302\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\autograd\__init__.py", line 99, in backward
    allow_unreachable=True)  # allow_unreachable flag

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [4, 20]], which is output 0 of SigmoidBackward, is at version 54; expected version 0 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).