I’m training a network using a custom loss function. And it gives a `RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn`

. It seems that the indices from `max`

is not differentiable which i have to use for loss function.

Does anyone have idea to fix it?

This is my code:

```
class MyLoss(nn. Module):
def __init__(self):
super(MyLoss, self).__init__()
def forwad(self, d, m_label, n_label):
# d size : [N, C, H, W], from softmax output
# m_label size : [N, 1, H, W]
# n_label size : [N, 1, H, W]
# get the predicted class
pred = d.max(1)[1]
pred = pred.unsqueeze(1)
n_pred = 2.7*pred + m_label
# calc image grad along x, y
m_dx = m_label[:, :, 1:, :] - m_label[:, :, :-1, :]
m_dy = m_label[:, :, :, 1:] - m_label[:, :, :, :-1]
n_dx = n_pred[:, :, 1:, :] - n_pred[:, :, :-1, :]
n_dy = n_pred[:, :, :, 1:] - n_pred[:, :, :, :-1]
# loss1: minimize the difference of grad of m_label and n_pred
loss1 = torch.mean(((n_dx - m_dx).abs().sum()
+ (n_dy - m_dy).abs().sum()))
# loss2: minimize the difference of n_pred and n_label
loss2 = nn.functional.l1_loss(n_pred, n_label)
return loss1 + loss2
```