66 lines
1.4 KiB
Julia
66 lines
1.4 KiB
Julia
@net type TLP
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first
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second
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function (x)
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l1 = σ(first(x))
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l2 = softmax(second(l1))
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end
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end
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function test_tupleio(bk)
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@testset "Tuple I/O" begin
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val = [1,2,3]
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tup = ([1,2,3],[4,5,6])
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@test bk(@net x -> (identity(x),))(val) == (val,)
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@test bk(@net x -> x[1].*x[2])(tup) == [4,10,18]
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end
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end
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function test_recurrence(bk)
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@testset "Recurrence" begin
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seq = batchone(Seq(rand(10) for i = 1:3))
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r = unroll(Recurrent(10, 5), 3)
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rm = bk(r)
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@test r(seq) ≈ rm(seq)
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end
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end
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function test_back(bk)
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@testset "Backward Pass" begin
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xs, ys = rand(1, 20), rand(1, 20)
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d = Affine(20, 10)
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dm = bk(d)
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d′ = deepcopy(d)
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@test dm(xs) ≈ d(xs)
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@test dm(xs) ≈ d′(xs)
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Δ = back!(dm, randn(1, 10), xs)
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@test length(Δ[1]) == 20
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update!(dm, 0.1)
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@test dm(xs) ≈ d(xs)
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@test dm(xs) ≉ d′(xs)
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end
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end
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function test_stacktrace(bk)
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@testset "Stack Traces" begin
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model = TLP(Affine(10, 20), Affine(21, 15))
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dm = bk(model)
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e = try dm(rand(1, 10))
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catch e e end
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@test isa(e, DataFlow.Interpreter.Exception)
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@test e.trace[1].func == Symbol("Flux.Affine")
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@test e.trace[2].func == :TLP
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end
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end
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function test_anon(bk)
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@testset "Closures" begin
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x, y = rand(3), rand(5)
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model = bk(@net xs -> map(x -> x .* x, xs))
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@test all(model((x, y)) .≈ (x.*x, y.*y))
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end
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end
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