Flux.jl/test/optimise.jl

using Flux.Optimise
using Flux.Optimise: runall
using Zygote: Params, gradient
using Test
# @testset "Optimise" begin
#   w = randn(10, 10)
#   @testset for opt in [ADAMW(), ADAGrad(0.1), AdaMax(), ADADelta(0.9), AMSGrad(),
#                        NADAM(), Descent(0.1), ADAM(), Nesterov(), RMSProp(),
#                        Momentum()]
#     w′ = randn(10, 10)
#     loss(x) = Flux.mse(w*x, w′*x)
#     for t = 1: 10^5
#       θ = Params([w′])
#       θ̄ = gradient(() -> loss(rand(10)), θ)
#       Optimise.update!(opt, θ, θ̄)
#     end
#     @test Flux.mse(w, w′) < 0.01
#   end
# end

# @testset "Optimiser" begin
#   w = randn(10, 10)
#   @testset for Opt in [InvDecay, WeightDecay, ExpDecay]
#     w′ = param(randn(10, 10))
#     loss(x) = Flux.mse(w*x, w′*x)
#     opt = Optimiser(Opt(), ADAM(0.001))
#     for t = 1:10^5
#       l = loss(rand(10))
#       back!(l)
#       delta = Optimise.apply!(opt, w′.data, w′.grad)
#       w′.data .-= delta
#     end
#     @test Flux.mse(w, w′) < 0.01
#   end
# end

# @testset "Training Loop" begin
#   i = 0
#   l = 1
#
#   Flux.train!(() -> (sleep(0.1); i += 1; l),
#               (),
#               Iterators.repeated((), 100),
#               Descent(),
#               cb = Flux.throttle(() -> (i > 3 && Flux.stop()), 1))
#
#   @test 3 < i < 50
#
#   # Test multiple callbacks
#   x = 0
#   fs = [() -> (), () -> x = 1]
#   cbs = runall(fs)
#   cbs()
#   @test x == 1
# end