3 ms·
No, the parent example was sum(A * B)^2. I don't think you can use * if you want to backprop, for example in layer regularisation. You'd need to use only PyTor
by dunefox 6y ago
No, the parent example was sum(A * B)^2.
I don't think you can use * if you want to backprop, for example in layer regularisation. You'd need to use only PyTorch operations such as torch.mul(A, B).pow(2).sum().
The point here isn't that PyTorch can't look similar to Julia in this small example, rather that I can just use regular, concise Julia syntax - unlike in Python + PyTorch where I need to use PyTorch constructs that are outside of Python.
- mkl 6y agoWell, there's the notational ambiguity! In maths notation, Σ(x * y)^2 would mean Σ((x * y)^2), but in most programming notation, treating Σ as a function, it would be as you say. I'm going with the original formula in https://news.ycombinator.com/item?id=23508661 https://news.ycombinator.com/item?id=23508661. I don't know PyTorch, but regular Python, Numpy, Sympy, etc. seem very similar to Julia in this instance.
- dunefox 6y agoYou're right, that was my mistake - but I wouldn't say that it's extremely ambiguous in this case, I just wasn't consequent enough in reading the formula. > I don't know PyTorch, but regular Python, Numpy, Sympy, etc. seem very similar to Julia in this instance. If you need PyTorch to record the operations for the backward pass later on I believe you need to use PyTorch versions of *, +, etc.: torch.mul, torch.sub, torch.add, etc. In Julia you can just use built in functions and let Flux handle the backward pass.