Hello all, I have two variables a,b with requires_grad=True. The loss1 will take the a and b as inputs, while the loss2 will take the normalization of a and b as input. So, should I use a clone in this case? This is my implementation
a,b
loss1
a
b
loss2
loss1= loss1(a,b) a_norm = a / 255 b_norm = b/255 loss2 = loss2(a_norm, b_norm) loss_total = loss1+loss2
Hi,
You don’t need to clone or detach here. The code you have will work well.
@albanD: Thanks. But do you think the loss 1 will be received a normalization value after gradient update because we use a_norm= a/ 255 in forward
When you do a_norm = a / 255, you do not modify a in any way.
a_norm = a / 255