34 lines
831 B
Julia
34 lines
831 B
Julia
using Flux, Test
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function ngradient(f, xs::AbstractArray...)
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grads = zero.(xs)
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for (x, Δ) in zip(xs, grads), i in 1:length(x)
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δ = sqrt(eps())
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tmp = x[i]
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x[i] = tmp - δ/2
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y1 = f(xs...)
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x[i] = tmp + δ/2
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y2 = f(xs...)
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x[i] = tmp
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Δ[i] = (y2-y1)/δ
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end
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return grads
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end
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gradcheck(f, xs...) =
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all(isapprox.(ngradient(f, xs...),
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gradient(f, xs...), rtol = 1e-5, atol = 1e-5))
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gradtest(f, xs::AbstractArray...) = gradcheck((xs...) -> sum(sin.(f(xs...))), xs...)
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gradtest(f, dims...) = gradtest(f, rand.(Float64, dims)...)
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@testset "Zygote" begin
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@test gradtest(Flux.mse, rand(5,5), rand(5, 5))
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@test gradtest(Flux.crossentropy, rand(5,5), rand(5, 5))
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# @test gradtest(x -> Flux.normalise(x), rand(4,3))
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# @test gradtest(x -> Flux.normalise(x, dims = 2), rand(3,4))
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end
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