Adjoints give incorrect values if t values are not unique #335
Description
MWE https://discourse.julialang.org/t/different-results-between-zygote-forwarddiff-and-reversediff/46540
using DifferentialEquations, Flux, LinearAlgebra, DiffEqFlux, DiffEqSensitivity
using ForwardDiff
using Zygote
using ReverseDiff
function bimolecular!(du,u,p,t,dens,cons)
# unpack rates and constants
nᵣ = u[1]
k₁,k₋₁ = p
mᵣ,mₗ,A = dens
# model
du[1] = dnᵣ = A*k₁*mᵣ*mₗ - k₋₁*nᵣ
end
function run_model(model,p,data,densities,constants,t) # version of run function with multiple models
Σ_sol_stack = zeros(1, size(data,2))
for i in 1:size(data,1)
# run model with given densities
densities_i = densities[i,:]
f = (du,u,p,t) -> model(du,u,10 .^ p,t,densities_i,constants)
tmp_prob = ODEProblem(f,u₀,tspan,p)
tmp_sol = solve(tmp_prob,Vern7(),saveat=t, abstol=1e-8,reltol=1e-8)
# stack Σ of solution across n species
Σ_sol = sum(Array(tmp_sol),dims=1)
Σ_sol_stack = vcat(Σ_sol_stack,Σ_sol)
end
return Σ_sol_stack[2:end,:]
end
function loss(p,data,model,dens,cons,t)
Σ_sol = run_model(model,p,data,dens,cons,t)
sum(abs2, (Σ_sol - data)) #, Σ_sol
end
dataset = [ 0.25 0.0618754
0.25 0.040822
0.5 0.127833
0.5 0.198451
1.0 0.274437
1.0 0.223144
2.0 0.579818
2.0 0.653926
4.0 0.693147
4.0 0.776529
6.0 0.820981
6.0 0.776529
8.0 0.653926
8.0 0.776529
16.0 0.820981
16.0 0.733969]
t = dataset[:,1]
n = dataset[:,2]
densities = [25.0, 38.0, 1.0]
tspan = (0,maximum(t)+1)
u₀ = [0.0]
rates = [ -3.367837470456765, -0.2863777340019116]
loss_new = (p) -> loss(p,n',bimolecular!,densities',[],t)
loss_new(rates)
grad_zyg = Zygote.gradient(loss_new,rates)
grad_for = ForwardDiff.gradient(loss_new,rates)
grad_rev = ReverseDiff.gradient(loss_new,rates)
hes_for = ForwardDiff.hessian(loss_new,rates)
hes_rev = ReverseDiff.hessian(loss_new,rates)
hes_zyg = Zygote.hessian(loss_new,rates)