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Flux.jl
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tests with gradients

This commit is contained in:
thebhatman 2019-07-12 20:47:43 +05:30
parent 1fc584102d
commit 8d6028e27a
2 changed files with 10 additions and 14 deletions
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20
test/cuda/cudnn.jl
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@ -14,13 +14,11 @@ trainmode(f, x...) = forward(f, x...)[1]
@test cpu(data(cy)) ≈ data(y)
g = rand(size(y)...)
# Flux.back!(y, g)
# Flux.back!(cy, gpu(g))
g = gradient(()->sum(m(x)), params(m))
cg = gradient(()->sum(cm(cx), params(cm))
@test m.γ ≈ cpu(cm.γ)
@test m.β ≈ cpu(cm.β)
@test x ≈ cpu(x)
@test g.grads[m.γ] ≈ cpu(cg.grads[cm.γ])
@test g.grads[m.β] ≈ cpu(cg.grads[cm.β])
end
@testset "2D Input" begin
@ -36,12 +34,10 @@ trainmode(f, x...) = forward(f, x...)[1]
@test cpu(data(cy)) ≈ data(y)
g = rand(size(y)...)
#Flux.back!(y, g)
#Flux.back!(cy, gpu(g))
g = gradient(()->sum(m(x)), params(m))
cg = gradient(()->sum(cm(cx), params(cm))
@test m.γ ≈ cpu(cm.γ)
@test m.β ≈ cpu(cm.β)
@test x ≈ cpu(x)
@test g.grads[m.γ] ≈ cpu(cg.grads[cm.γ])
@test g.grads[m.β] ≈ cpu(cg.grads[cm.β])
end
end

4
test/layers/normalisation.jl
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@ -6,8 +6,8 @@ trainmode(f, x...) = forward(f, x...)[1]
@testset "Dropout" begin
x = [1.,2.,3.]
@test x == Dropout(0.1)(x)
@test x == trainmode(Dropout(0), (x))
@test zero(x) == trainmode(Dropout(1), (x))
@test x == trainmode(Dropout(0), x)
@test zero(x) == trainmode(Dropout(1), x)
x = rand(100)
m = Dropout(0.9)