I wanted to reproduce:
from the paper https://arxiv.org/pdf/1312.6199.pdf. I was wondering, how does one actually implement this in pytorch? My main confusion is that for
loss_f I am using a
torch.nn.CrossEntropy() criterion for example. Do i just need to change the code that I already have from:
loss = criterion(outputs+r, labels) loss.backward()
loss = criterion(outputs+r, labels) loss = loss + c * r.norm(2) loss.backward()
or something along those lines. I know its not quite right cuz I did not explicitly show how I implemented
x+r or the hypercube constraint but those are parts that I still need to figure out.