Merge pull request #710 from johnnychen94/master
naive implementation of activations
This commit is contained in:
Mike J Innes
GitHub
commit
54d9229be9
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@ -40,7 +40,24 @@ function Base.show(io::IO, c::Chain)
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print(io, ")")
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end
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activations(c::Chain, x) = accumulate((x, m) -> m(x), c.layers, init = x)
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# This is a temporary and naive implementation
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# it might be replaced in the future for better performance
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# see issue https://github.com/FluxML/Flux.jl/issues/702
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# Johnny Chen -- @johnnychen94
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"""
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activations(c::Chain, input)
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Calculate the forward results of each layers in Chain `c` with `input` as model input.
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"""
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function activations(c::Chain, input)
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rst = []
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for l in c
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x = get(rst, length(rst), input)
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push!(rst, l(x))
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end
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return rst
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end
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"""
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Dense(in::Integer, out::Integer, σ = identity)
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@ -1,63 +1,75 @@
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using Test, Random
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import Flux: activations
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@testset "basic" begin
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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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@testset "helpers" begin
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@testset "activations" begin
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dummy_model = Chain(Dense(10,5,σ),Dense(5,2),softmax)
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x = rand(10)
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@test activations(Chain(), x) == []
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@test activations(dummy_model, x)[1] == dummy_model[1](x)
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@test activations(dummy_model, x)[2] == x |> dummy_model[1] |> dummy_model[2]
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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 "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 "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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@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 "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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@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 "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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@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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end
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