Hey guys,
I have probabilities as parameters in my model and I wish to normalise them (divide by their norm so they sum up to 1) as they are being optimised. Probably I should impose this constraint during training with:
with torch.no_grad():
However  I haven’t found a way to so, as any assignments, like
model.probs = model.probs/model.probs.sum()
don’t work, bc you cannot assign a float to a parameter. Any suggestions?
Thank you in advance,
Nikos