23 lines
478 B
Julia
23 lines
478 B
Julia
function gradient(f, xs::AbstractArray...)
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xs = track.(xs)
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back!(f(xs...))
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grad.(xs)
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end
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function ngradient(f, xs::AbstractArray...)
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y = f(xs...)
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grads = zeros.(xs)
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for (x, Δ) in zip(xs, grads)
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for i in 1:length(x)
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δ = sqrt(eps())
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tmp, x[i] = x[i], x[i]+δ
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y′ = f(xs...)
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x[i] = tmp
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Δ[i] = (y′-y)/δ
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end
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end
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return grads
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end
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gradcheck(f, xs...) = all(isapprox.(ngradient(f, xs...), gradient(f, xs...), rtol = 1e-6))
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