I’m having a hard time figuring out why this works: using Zygote W, b = rand(2, 3), rand(2) predict(x) = W*x .+ b g = gradient(() -> sum(predict([1,2,3])), Params([W, b])) g[W], g[b] but this doesn’t: using Zygote a = 2 x = 2 f(x) = x^a gp = gradient(() -> f(x), Params(a)) gp[a] I get the error: …

gradient((a1,a2)->F(L1,L2; a1=a1,a2=a2), a1,a2) should do the trick, no?

I think I figured it out. Everything has to be an array except the output of the function. So this now works: using Zygote a = [2] x = [2] f(x) = x.^a[1] gp = gradient(() -> sum(f(x)), Params([a])) gp[a]

Your second code works, but using arrays is quite a drag on performance: # Integer version julia> @btime $(Ref(2))[]^$(Ref(2))[] 3.369 ns (0 allocations: 0 bytes) 4 # Array version julia> @btime [2].^[2][1]; 82.958 ns (3 allocations: 288 bytes) The easiest way to get what you want is obviousl…

Thanks for offering. This is what I am trying to accomplish: I have a function that I would normally write as F(L1, L2; a1=1,a2=1) = a1*(L1^a2 + L2^a2)^(1/a2) a1 and a2 are parameters. I’m trying to get the derivatives with respect to a1 and a2. I could write it using arrays: F(L; a=ones(1,2)) =…

I’m no expert, but I would say the main occasion when Params comes in handy is if the function to differentiate consists of many nested, parametrised functions (e.g. a deep neural network). In this case, it can be annoying to explicitly pass the parameters through the callstack, and Params provides …

Help with Zygote and parameters

New to Julia

amrods July 1, 2020, 9:05am 6

That works. Then I’m unsure when it is necessary to employ Params. Could you explain a little about that?

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Help with Zygote and parameters

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