I'm getting an error using sciml to train a DiffEqFlux tutorial. I used the same exact code with some modifications last summer with no problems but can't seem to find a solution. I am using Julia v.1.7.1
using DiffEqFlux, OrdinaryDiffEq, Flux, Optim, Plots
u0 = Float32[2.0; 0.0]
datasize = 30
tspan = (0.0f0, 1.5f0)
tsteps = range(tspan[1], tspan[2], length = datasize)
function trueODEfunc(du, u, p, t)
true_A = [-0.1 2.0; -2.0 -0.1]
du .= ((u.^3)'true_A)'
end
prob_trueode = ODEProblem(trueODEfunc, u0, tspan)
ode_data = Array(solve(prob_trueode, Tsit5(), saveat = tsteps))
dudt2 = FastChain((x, p) -> x.^3,
FastDense(2, 50, tanh),
FastDense(50, 2))
prob_neuralode = NeuralODE(dudt2, tspan, Tsit5(), saveat = tsteps)
function predict_neuralode(p)
Array(prob_neuralode(u0, p))
end
function loss_neuralode(p)
pred = predict_neuralode(p)
loss = sum(abs2, ode_data .- pred)
return loss, pred
end
# Callback function to observe training
list_plots = []
iter = 0
callback = function ( l, pred; doplot = false)
global list_plots, iter
if iter == 0
list_plots = []
end
iter += 1
display(l)
# plot current prediction against data
plt = scatter(tsteps, ode_data[1,:], label = "data")
scatter!(plt, tsteps, pred[1,:], label = "prediction")
push!(list_plots, plt)
if doplot
display(plot(plt))
end
return false
end
result_neuralode = DiffEqFlux.sciml_train(loss_neuralode, prob_neuralode.p,
ADAM(0.05), cb = callback,
maxiters = 300)
With the error being:
MethodError: no method matching (OptimizationFunction{false, GalacticOptim.AutoZygote, OptimizationFunction{true, GalacticOptim.AutoZygote, DiffEqFlux.var"#84#89"{typeof(loss_neuralode)}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, GalacticOptim.var"#268#278"{GalacticOptim.var"#267#277"{OptimizationFunction{true, GalacticOptim.AutoZygote, DiffEqFlux.var"#84#89"{typeof(loss_neuralode)}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Nothing}}, GalacticOptim.var"#271#281"{GalacticOptim.var"#267#277"{OptimizationFunction{true, GalacticOptim.AutoZygote, DiffEqFlux.var"#84#89"{typeof(loss_neuralode)}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Nothing}}, GalacticOptim.var"#276#286", Nothing, Nothing, Nothing})(::OptimizationFunction{true, GalacticOptim.AutoZygote, DiffEqFlux.var"#84#89"{typeof(loss_neuralode)}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, ::GalacticOptim.AutoZygote, ::GalacticOptim.var"#268#278"{GalacticOptim.var"#267#277"{OptimizationFunction{true, GalacticOptim.AutoZygote, DiffEqFlux.var"#84#89"{typeof(loss_neuralode)}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Nothing}}, ::GalacticOptim.var"#271#281"{GalacticOptim.var"#267#277"{OptimizationFunction{true, GalacticOptim.AutoZygote, DiffEqFlux.var"#84#89"{typeof(loss_neuralode)}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Nothing}}, ::GalacticOptim.var"#276#286", ::Nothing, ::Nothing, ::Nothing)
Stacktrace:
[1] instantiate_function(f::Function, x::Vector{Float32}, ::GalacticOptim.AutoZygote, p::Nothing, num_cons::Int64)
@ GalacticOptim C:\Users\User 1\.julia\packages\GalacticOptim\fow0r\src\function\zygote.jl:40
[2] instantiate_function(f::Function, x::Vector{Float32}, ::GalacticOptim.AutoZygote, p::Nothing)
@ GalacticOptim C:\Users\User 1\.julia\packages\GalacticOptim\fow0r\src\function\zygote.jl:4
[3] sciml_train(::typeof(loss_neuralode), ::Vector{Float32}, ::ADAM, ::Nothing; lower_bounds::Nothing, upper_bounds::Nothing, maxiters::Int64, kwargs::Base.Pairs{Symbol, var"#43#45", Tuple{Symbol}, NamedTuple{(:cb,), Tuple{var"#43#45"}}})
@ DiffEqFlux C:\Users\User 1\.julia\packages\DiffEqFlux\gH716\src\train.jl:87
I'm not sure if it could be the specific version of Julia that I'm using. This is directly from a tutorial at https://docs.juliahub.com/DiffEqFlux/BdO4p/1.13.0/examples/LV-ODE/
Any thoughts?
The docs example link you have shared is of v1.13 while currently we're at v1.48 for DiffEqFlux. Since then there have been many breaking changes in DiffEqFlux and its dependencies. Specifically, FastChain, FastDense have been deprecated in favour of Lux.jl. sciml_train is deprecated to use Optimization.jl(previously GalacticOptim.jl) directly.
I would recommend you to first run ]up in the julia repl. That should update your environment.
Then after adding any required libraries you should be able to run this updated example:
using Lux, DiffEqFlux, DifferentialEquations, Optimization, OptimizationOptimJL, Random, Plots
rng = Random.default_rng()
u0 = Float32[2.0; 0.0]
datasize = 30
tspan = (0.0f0, 1.5f0)
tsteps = range(tspan[1], tspan[2], length = datasize)
function trueODEfunc(du, u, p, t)
true_A = [-0.1 2.0; -2.0 -0.1]
du .= ((u.^3)'true_A)'
end
prob_trueode = ODEProblem(trueODEfunc, u0, tspan)
ode_data = Array(solve(prob_trueode, Tsit5(), saveat = tsteps))
dudt2 = Lux.Chain(ActivationFunction(x -> x.^3),
Lux.Dense(2, 50, tanh),
Lux.Dense(50, 2))
p, st = Lux.setup(rng, dudt2)
prob_neuralode = NeuralODE(dudt2, tspan, Tsit5(), saveat = tsteps)
function predict_neuralode(p)
Array(prob_neuralode(u0, p, st)[1])
end
function loss_neuralode(p)
pred = predict_neuralode(p)
loss = sum(abs2, ode_data .- pred)
return loss, pred
end
callback = function (p, l, pred; doplot = true)
display(l)
# plot current prediction against data
plt = scatter(tsteps, ode_data[1,:], label = "data")
scatter!(plt, tsteps, pred[1,:], label = "prediction")
if doplot
display(plot(plt))
end
return false
end
# use Optimization.jl to solve the problem
adtype = Optimization.AutoZygote()
optf = Optimization.OptimizationFunction((x, p) -> loss_neuralode(x), adtype)
optprob = Optimization.OptimizationProblem(optf, Lux.ComponentArray(p))
result_neuralode = Optimization.solve(optprob,
ADAM(0.05),
callback = callback,
maxiters = 300)
optprob2 = remake(optprob,u0 = result_neuralode.u)
result_neuralode2 = Optimization.solve(optprob2,
LBFGS(),
allow_f_increases = false)
which will also be available in the docs soon.