6 ms·

I'm of the same opinion. In Julia it would be sum(A * B) ^ 2 If you define sum as Σ then you can do Σ(A * B) ^ 2

enriquto 6y ago
yes I like Julia very much because it seems to put the natural instincts of mathematicians upfront. When programming in Julia you do not have the impression of fighting against the language continuously. Everything that you want to do is accessible, and it is alright if you implement your computation in the most natural way possible. If I try to do that in python, I always end with an ugly mess of numba, numpy and calls to C code used at different places for different reasons.
mkl 6y ago
I think you mean sum((A*B)^2)? That's one character shorter than pytorch if you import sum out of it, and Σ = torch.sum would be fine in Python too.
- 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 ago
    Well, 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 ago
      You'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.

yes I like Julia very much because it seems to put the natural instincts of mathematicians upfront. When programming in Julia you do not have the impression of fighting against the language continuously. Everything that you want to do is accessible, and it is alright if you implement your computation in the most natural way possible. If I try to do that in python, I always end with an ugly mess of numba, numpy and calls to C code used at different places for different reasons.

mkl 6y ago

I think you mean sum((A*B)^2)? That's one character shorter than pytorch if you import sum out of it, and Σ = torch.sum would be fine in Python too.

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 ago
  Well, 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 ago
    You'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.