Flux.jl/test/optimise.jl

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using Flux.Optimise
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using Flux.Optimise: runall
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using Flux.Tracker
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using Test
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@testset "Optimise" begin
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w = randn(10, 10)
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@testset for opt in [ADAMW(), ADAGrad(0.1), AdaMax(), ADADelta(0.9), AMSGrad(),
NADAM(), Descent(0.1), ADAM(), Nesterov(), RMSProp(),
Momentum()]
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w = param(randn(10, 10))
loss(x) = Flux.mse(w*x, w*x)
for t = 1: 10^5
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θ = Params([w])
θ̄ = gradient(() -> loss(rand(10)), θ)
Optimise.update!(opt, θ, θ̄)
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end
@test Flux.mse(w, w) < 0.01
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end
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end
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@testset "Optimiser" begin
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w = randn(10, 10)
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@testset for Opt in [InvDecay, WeightDecay, ExpDecay]
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w = param(randn(10, 10))
loss(x) = Flux.mse(w*x, w*x)
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opt = Optimiser(Opt(), ADAM(0.001))
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for t = 1:10^5
l = loss(rand(10))
back!(l)
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delta = Optimise.apply!(opt, w.data, w.grad)
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w.data .-= delta
end
@test Flux.mse(w, w) < 0.01
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end
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end
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@testset "Training Loop" begin
i = 0
l = param(1)
Flux.train!(() -> (sleep(0.1); i += 1; l),
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(),
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Iterators.repeated((), 100),
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Descent(),
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cb = Flux.throttle(() -> (i > 3 && Flux.stop()), 1))
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@test 3 < i < 50
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# Test multiple callbacks
x = 0
fs = [() -> (), () -> x = 1]
cbs = runall(fs)
cbs()
@test x == 1
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end
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@testset "ExpDecay" begin
w = randn(10, 10)
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o = ExpDecay(0.1, 0.1, 1000, 1e-4)
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w1 = param(randn(10,10))
loss(x) = Flux.mse(w*x, w1*x)
flag = 1
decay_steps = []
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for t = 1:10^5
l = loss(rand(10))
back!(l)
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prev_eta = o.eta
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prev_grad = collect(w1.grad)
delta = Optimise.apply!(o, w1.data, w1.grad)
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w1.data .-= delta
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new_eta = o.eta
if new_eta != prev_eta
push!(decay_steps, t)
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end
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array = fill(o.eta, size(prev_grad))
if array .* prev_grad != delta
flag = 0
end
end
@test flag == 1
# Test to check if decay happens at decay steps. Eta reaches clip value eventually.
ground_truth = []
for i in 1:11
push!(ground_truth, 1000*i) # Expected decay steps for this example.
end
@test decay_steps == ground_truth
@test o.eta == o.clip
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end