111 lines
3.5 KiB
Julia
111 lines
3.5 KiB
Julia
using Test, Random
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import Flux: activations
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@testset "basic" begin
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@testset "helpers" begin
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@testset "activations" begin
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dummy_model = Chain(x->x.^2, x->x .- 3, x -> tan.(x))
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x = randn(10)
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@test activations(dummy_model, x)[1] == x.^2
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@test activations(dummy_model, x)[2] == (x.^2 .- 3)
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@test activations(dummy_model, x)[3] == tan.(x.^2 .- 3)
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@test activations(Chain(), x) == ()
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@test activations(Chain(identity, x->:foo), x)[2] == :foo # results include `Any` type
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end
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end
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@testset "Chain" begin
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@test_nowarn Chain(Dense(10, 5, σ), Dense(5, 2))(randn(10))
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@test_throws DimensionMismatch Chain(Dense(10, 5, σ),Dense(2, 1))(randn(10))
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# numeric test should be put into testset of corresponding layer
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end
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@testset "Activations" begin
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c = Chain(Dense(3,5,relu), Dense(5,1,relu))
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X = Float32.([1.0; 1.0; 1.0])
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@test_nowarn gradient(()->Flux.activations(c, X)[2][1], params(c))
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end
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@testset "Dense" begin
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@test length(Dense(10, 5)(randn(10))) == 5
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@test_throws DimensionMismatch Dense(10, 5)(randn(1))
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@test_throws MethodError Dense(10, 5)(1) # avoid broadcasting
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@test_throws MethodError Dense(10, 5).(randn(10)) # avoid broadcasting
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@test Dense(10, 1, identity, initW = ones, initb = zeros)(ones(10,1)) == 10*ones(1, 1)
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@test Dense(10, 1, identity, initW = ones, initb = zeros)(ones(10,2)) == 10*ones(1, 2)
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@test Dense(10, 2, identity, initW = ones, initb = zeros)(ones(10,1)) == 10*ones(2, 1)
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@test Dense(10, 2, identity, initW = ones, initb = zeros)([ones(10,1) 2*ones(10,1)]) == [10 20; 10 20]
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end
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@testset "Diagonal" begin
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@test length(Flux.Diagonal(10)(randn(10))) == 10
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@test length(Flux.Diagonal(10)(1)) == 10
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@test length(Flux.Diagonal(10)(randn(1))) == 10
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@test_throws DimensionMismatch Flux.Diagonal(10)(randn(2))
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@test Flux.Diagonal(2)([1 2]) == [1 2; 1 2]
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@test Flux.Diagonal(2)([1,2]) == [1,2]
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@test Flux.Diagonal(2)([1 2; 3 4]) == [1 2; 3 4]
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end
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@testset "Maxout" begin
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# Note that the normal common usage of Maxout is as per the docstring
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# These are abnormal constructors used for testing purposes
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@testset "Constructor" begin
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mo = Maxout(() -> identity, 4)
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input = rand(40)
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@test mo(input) == input
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end
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@testset "simple alternatives" begin
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mo = Maxout((x -> x, x -> 2x, x -> 0.5x))
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input = rand(40)
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@test mo(input) == 2*input
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end
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@testset "complex alternatives" begin
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mo = Maxout((x -> [0.5; 0.1]*x, x -> [0.2; 0.7]*x))
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input = [3.0 2.0]
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target = [0.5, 0.7].*input
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@test mo(input) == target
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end
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@testset "params" begin
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mo = Maxout(()->Dense(32, 64), 4)
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ps = params(mo)
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@test length(ps) == 8 #4 alts, each with weight and bias
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end
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end
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@testset "SkipConnection" begin
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@testset "zero sum" begin
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input = randn(10, 10, 10, 10)
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@test SkipConnection(x -> zeros(size(x)), (a,b) -> a + b)(input) == input
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end
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@testset "concat size" begin
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input = randn(10, 2)
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@test size(SkipConnection(Dense(10,10), (a,b) -> cat(a, b, dims = 2))(input)) == (10,4)
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end
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end
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@testset "output dimensions" begin
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m = Chain(Conv((3, 3), 3 => 16), Conv((3, 3), 16 => 32))
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@test Flux.outdims(m, (10, 10)) == (6, 6)
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m = Dense(10, 5)
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@test Flux.outdims(m, (5, 2)) == (5,)
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@test Flux.outdims(m, (10,)) == (5,)
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m = Flux.Diagonal(10)
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@test Flux.outdims(m, (10,)) == (10,)
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m = Maxout(() -> Conv((3, 3), 3 => 16), 2)
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@test Flux.outdims(m, (10, 10)) == (8, 8)
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end
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end
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