https://github.com/JuliaDiffEq/DiffEqFlux.jl
Revision 9af1cd7313fe1b3e7d235bc9f42212961ae93a1e authored by Christopher Rackauckas on 08 April 2020, 18:36:50 UTC, committed by GitHub on 08 April 2020, 18:36:50 UTC
Add QuadDIRECT global optimizer
Tip revision: 9af1cd7313fe1b3e7d235bc9f42212961ae93a1e authored by Christopher Rackauckas on 08 April 2020, 18:36:50 UTC
Merge pull request #220 from ranjanan/RA/quaddirect
Merge pull request #220 from ranjanan/RA/quaddirect
Tip revision: 9af1cd7
neural_de_gpu.jl
using OrdinaryDiffEq, StochasticDiffEq, Flux, DiffEqSensitivity, DiffEqFlux, Zygote, Test, CuArrays
CuArrays.allowscalar(false)
mp = Chain(Dense(2,2)) |> gpu
x = Float32[2.; 0.] |> gpu
xs = Float32.(hcat([0.; 0.], [1.; 0.], [2.; 0.])) |> gpu
tspan = (0.0f0,25.0f0)
dudt = Chain(Dense(2,50,tanh),Dense(50,2)) |> gpu
NeuralODE(dudt,tspan,Tsit5(),save_everystep=false,save_start=false)(x)
NeuralODE(dudt,tspan,Tsit5(),saveat=0.1)(x)
NeuralODE(dudt,tspan,Tsit5(),saveat=0.1,sensealg=TrackerAdjoint())(x)
NeuralODE(dudt,tspan,Tsit5(),save_everystep=false,save_start=false)(xs)
NeuralODE(dudt,tspan,Tsit5(),saveat=0.1)(xs)
NeuralODE(dudt,tspan,Tsit5(),saveat=0.1,sensealg=TrackerAdjoint())(xs)
node = NeuralODE(dudt,tspan,Tsit5(),save_everystep=false,save_start=false)
grads = Zygote.gradient(()->sum(node(x)),Flux.params(x,node))
@test ! iszero(grads[x])
@test ! iszero(grads[node.p])
grads = Zygote.gradient(()->sum(node(xs)),Flux.params(xs,node))
@test ! iszero(grads[xs])
@test ! iszero(grads[node.p])
node = NeuralODE(dudt,tspan,Tsit5(),save_everystep=false,save_start=false,sensealg=TrackerAdjoint())
grads = Zygote.gradient(()->sum(Array(node(x))),Flux.params(x,node))
@test ! iszero(grads[x])
@test ! iszero(grads[node.p])
grads = Zygote.gradient(()->sum(node(xs)),Flux.params(xs,node))
@test ! iszero(grads[xs])
@test ! iszero(grads[node.p])
node = NeuralODE(dudt,tspan,Tsit5(),save_everystep=false,save_start=false,sensealg=BacksolveAdjoint())
grads = Zygote.gradient(()->sum(node(x)),Flux.params(x,node))
@test ! iszero(grads[x])
@test ! iszero(grads[node.p])
grads = Zygote.gradient(()->sum(node(xs)),Flux.params(xs,node))
@test ! iszero(grads[xs])
@test ! iszero(grads[node.p])
# Adjoint
@testset "adjoint mode" begin
node = NeuralODE(dudt,tspan,Tsit5(),save_everystep=false,save_start=false)
grads = Zygote.gradient(()->sum(node(x)),Flux.params(x,node))
@test ! iszero(grads[x])
@test ! iszero(grads[node.p])
grads = Zygote.gradient(()->sum(node(xs)),Flux.params(xs,node))
@test ! iszero(grads[xs])
@test ! iszero(grads[node.p])
node = NeuralODE(dudt,tspan,Tsit5(),saveat=0.0:0.1:10.0)
grads = Zygote.gradient(()->sum(node(x)),Flux.params(x,node))
@test ! iszero(grads[x])
@test ! iszero(grads[node.p])
grads = Zygote.gradient(()->sum(node(xs)),Flux.params(xs,node))
@test ! iszero(grads[xs])
@test ! iszero(grads[node.p])
node = NeuralODE(dudt,tspan,Tsit5(),saveat=1f-1)
grads = Zygote.gradient(()->sum(node(x)),Flux.params(x,node))
@test ! iszero(grads[x])
@test ! iszero(grads[node.p])
grads = Zygote.gradient(()->sum(node(xs)),Flux.params(xs,node))
@test ! iszero(grads[xs])
@test ! iszero(grads[node.p])
end
NeuralDSDE(dudt,mp,(0.0f0,2.0f0),SOSRI(),saveat=0.0:0.1:2.0)(x)
sode = NeuralDSDE(dudt,mp,(0.0f0,2.0f0),SOSRI(),saveat=Float32.(0.0:0.1:2.0),dt=1f-1)
@test_broken grads = Zygote.gradient(()->sum(sode(x)),Flux.params(x,sode)) isa Tuple
#=
@test ! iszero(grads[x])
@test ! iszero(grads[sode.p])
grads = Zygote.gradient(()->sum(sode(xs)),Flux.params(xs,sode))
@test ! iszero(grads[xs])
@test ! iszero(grads[sode.p])
=#
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