188 lines
4.7 KiB
Julia
188 lines
4.7 KiB
Julia
using Flux: testmode!
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using Flux.Tracker: data
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@testset "Dropout" begin
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x = [1.,2.,3.]
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@test x == testmode!(Dropout(0.1))(x)
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@test x == Dropout(0)(x)
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@test zero(x) == Dropout(1)(x)
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x = rand(100)
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m = Dropout(0.9)
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y = m(x)
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@test count(a->a==0, y) > 50
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testmode!(m)
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y = m(x)
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@test count(a->a==0, y) == 0
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testmode!(m, false)
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y = m(x)
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@test count(a->a==0, y) > 50
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x = rand(100)
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m = Chain(Dense(100,100),
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Dropout(0.9))
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y = m(x)
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@test count(a->a == 0, y) > 50
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testmode!(m)
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y = m(x)
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@test count(a->a == 0, y) == 0
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end
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@testset "BatchNorm" begin
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let m = BatchNorm(2), x = param([1 3 5;
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2 4 6])
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@test m.β.data == [0, 0] # initβ(2)
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@test m.γ.data == [1, 1] # initγ(2)
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# initial m.σ is 1
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# initial m.μ is 0
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@test m.active
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# @test m(x).data ≈ [-1 -1; 0 0; 1 1]'
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m(x)
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# julia> x
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# 2×3 Array{Float64,2}:
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# 1.0 3.0 5.0
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# 2.0 4.0 6.0
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#
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# μ of batch will be
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# (1. + 3. + 5.) / 3 = 3
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# (2. + 4. + 6.) / 3 = 4
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#
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# ∴ update rule with momentum:
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# .1 * 3 + 0 = .3
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# .1 * 4 + 0 = .4
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@test m.μ ≈ reshape([0.3, 0.4], 2, 1)
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# julia> .1 .* var(x, dims = 2, corrected=false) .* (3 / 2).+ .9 .* [1., 1.]
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# 2×1 Array{Float64,2}:
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# 1.3
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# 1.3
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@test m.σ² ≈ .1 .* var(x.data, dims = 2, corrected=false) .* (3 / 2).+ .9 .* [1., 1.]
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testmode!(m)
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@test !m.active
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x′ = m(x).data
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@test isapprox(x′[1], (1 .- 0.3) / sqrt(1.3), atol = 1.0e-5)
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end
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# with activation function
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let m = BatchNorm(2, sigmoid), x = param([1 3 5;
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2 4 6])
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@test m.active
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m(x)
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testmode!(m)
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@test !m.active
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y = m(x).data
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@test isapprox(y, data(sigmoid.((x .- m.μ) ./ sqrt.(m.σ² .+ m.ϵ))), atol = 1.0e-7)
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end
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let m = BatchNorm(2), x = param(reshape(1:6, 3, 2, 1))
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y = reshape(permutedims(x, [2, 1, 3]), 2, :)
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y = permutedims(reshape(m(y), 2, 3, 1), [2, 1, 3])
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@test m(x) == y
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end
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let m = BatchNorm(2), x = param(reshape(1:12, 2, 3, 2, 1))
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y = reshape(permutedims(x, [3, 1, 2, 4]), 2, :)
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y = permutedims(reshape(m(y), 2, 2, 3, 1), [2, 3, 1, 4])
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@test m(x) == y
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end
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let m = BatchNorm(2), x = param(reshape(1:24, 2, 2, 3, 2, 1))
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y = reshape(permutedims(x, [4, 1, 2, 3, 5]), 2, :)
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y = permutedims(reshape(m(y), 2, 2, 2, 3, 1), [2, 3, 4, 1, 5])
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@test m(x) == y
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end
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let m = BatchNorm(32), x = randn(Float32, 416, 416, 32, 1);
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m(x)
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@test (@allocated m(x)) < 100_000_000
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end
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end
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@testset "InstanceNorm" begin
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# helper functions
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expand_inst = (x, as) -> reshape(repeat(x, outer=[1, as[length(as)]]), as...)
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# begin tests
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let m = InstanceNorm(2), sizes = (3, 2, 2),
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x = param(reshape(collect(1:prod(sizes)), sizes))
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@test m.β.data == [0, 0] # initβ(2)
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@test m.γ.data == [1, 1] # initγ(2)
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@test m.active
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m(x)
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#julia> x
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#[:, :, 1] =
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# 1.0 4.0
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# 2.0 5.0
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# 3.0 6.0
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#
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#[:, :, 2] =
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# 7.0 10.0
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# 8.0 11.0
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# 9.0 12.0
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#
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# μ will be
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# (1. + 2. + 3.) / 3 = 2.
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# (4. + 5. + 6.) / 3 = 5.
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#
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# (7. + 8. + 9.) / 3 = 8.
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# (10. + 11. + 12.) / 3 = 11.
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#
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# ∴ update rule with momentum:
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# (1. - .1) * 0 + .1 * (2. + 8.) / 2 = .5
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# (1. - .1) * 0 + .1 * (5. + 11.) / 2 = .8
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@test m.μ ≈ [0.5, 0.8]
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# momentum * var * num_items / (num_items - 1) + (1 - momentum) * sigma_sq
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# julia> reshape(mean(.1 .* var(x.data, dims = 1, corrected=false) .* (3 / 2), dims=3), :) .+ .9 .* 1.
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# 2-element Array{Float64,1}:
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# 1.
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# 1.
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@test m.σ² ≈ reshape(mean(.1 .* var(x.data, dims = 1, corrected=false) .* (3 / 2), dims=3), :) .+ .9 .* 1.
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testmode!(m)
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@test !m.active
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x′ = m(x).data
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@test isapprox(x′[1], (1 - 0.5) / sqrt(1. + 1f-5), atol = 1.0e-5)
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end
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# with activation function
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let m = InstanceNorm(2, sigmoid), sizes = (3, 2, 2),
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x = param(reshape(collect(1:prod(sizes)), sizes))
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affine_shape = collect(sizes)
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affine_shape[1] = 1
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@test m.active
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m(x)
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testmode!(m)
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@test !m.active
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y = m(x).data
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@test isapprox(y, data(sigmoid.((x .- expand_inst(m.μ, affine_shape)) ./ sqrt.(expand_inst(m.σ², affine_shape) .+ m.ϵ))), atol = 1.0e-7)
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end
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let m = InstanceNorm(2), sizes = (2, 4, 1, 2, 3),
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x = param(reshape(collect(1:prod(sizes)), sizes))
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y = reshape(permutedims(x, [3, 1, 2, 4, 5]), :, 2, 3)
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y = reshape(m(y), sizes...)
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@test m(x) == y
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end
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let m = BatchNorm(32), x = randn(Float32, 416, 416, 32, 1);
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m(x)
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@test (@allocated m(x)) < 100_000_000
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end
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end
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