2017-10-12 08:31:38 +00:00
|
|
|
|
using Flux.Optimise
|
2018-11-08 13:14:57 +00:00
|
|
|
|
using Flux.Optimise: runall
|
2019-03-08 15:00:32 +00:00
|
|
|
|
using Zygote: Params, gradient
|
2018-08-11 12:54:59 +00:00
|
|
|
|
using Test
|
2019-03-08 15:00:32 +00:00
|
|
|
|
# @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
|
2017-12-13 18:24:56 +00:00
|
|
|
|
|
2019-03-08 15:00:32 +00:00
|
|
|
|
# @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
|
2018-09-16 12:04:51 +00:00
|
|
|
|
|
2019-03-08 15:00:32 +00:00
|
|
|
|
# @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
|
|
|
|
|
#
|
|
|
|
|
# @testset "ExpDecay" begin
|
|
|
|
|
# w = randn(10, 10)
|
|
|
|
|
# o = ExpDecay(0.1, 0.1, 1000, 1e-4)
|
|
|
|
|
# w1 = param(randn(10,10))
|
|
|
|
|
# loss(x) = Flux.mse(w*x, w1*x)
|
|
|
|
|
# flag = 1
|
|
|
|
|
# decay_steps = []
|
|
|
|
|
# for t = 1:10^5
|
|
|
|
|
# l = loss(rand(10))
|
|
|
|
|
# back!(l)
|
|
|
|
|
# prev_eta = o.eta
|
|
|
|
|
# prev_grad = collect(w1.grad)
|
|
|
|
|
# delta = Optimise.apply!(o, w1.data, w1.grad)
|
|
|
|
|
# w1.data .-= delta
|
|
|
|
|
# new_eta = o.eta
|
|
|
|
|
# if new_eta != prev_eta
|
|
|
|
|
# push!(decay_steps, t)
|
|
|
|
|
# end
|
|
|
|
|
# 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
|
|
|
|
|
# end
|