# UODE error

**URL:** <https://discourse.julialang.org/t/uode-error/105429>\
**Category:** General Usage\
**Created:** [October 26, 2023, 10:07am UTC](https://discourse.julialang.org/t/uode-error/105429 "2023-10-26T10:07:42Z")\
**Posts on this page:** 8\
**Page:** 1

<div class="post-metadata">

**Author:** ![Marco\_Nesta](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/marco_nesta/32/50318_2.png) [@Marco\_Nesta](https://discourse.julialang.org/u/Marco_Nesta)\
**Post date:** [October 26, 2023, 10:07am UTC](https://discourse.julialang.org/t/uode-error/105429/1 "2023-10-26T10:07:42Z")

</div>

following this link [signal (11.1): Segmentation fault in uODE · Issue #851 · SciML/DiffEqFlux.jl · GitHub](https://github.com/SciML/DiffEqFlux.jl/issues/851) I tried the code of the solution proposed by @ChrisRackauckas , but I have this error. can anyone help me?

```julia
ERROR: ArgumentError: broadcasting over dictionaries and `NamedTuple`s is reserved
Stacktrace:
 [1] broadcastable(#unused#::NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4), NTuple{4, NamedTuple{(:weight, :bias), Tuple{Base.ReshapedArray{Float64, 2, SubArray{Float64, 1, Vector{Float64}, Tuple{UnitRange{Int64}}, true}, Tuple{}}, Base.ReshapedArray{Float64, 2, SubArray{Float64, 1, Vector{Float64}, Tuple{UnitRange{Int64}}, true}, Tuple{}}}}}})
   @ Base.Broadcast .\broadcast.jl:718
 [2] broadcasted(::Function, ::NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4), NTuple{4, NamedTuple{(:weight, :bias), Tuple{Base.ReshapedArray{Float64, 2, SubArray{Float64, 1, Vector{Float64}, Tuple{UnitRange{Int64}}, true}, Tuple{}}, Base.ReshapedArray{Float64, 2, SubArray{Float64, 1, Vector{Float64}, Tuple{UnitRange{Int64}}, true}, Tuple{}}}}}})
   @ Base.Broadcast .\broadcast.jl:1309
 [3] (::Optimization.var"#275#284"{Optimization.var"#274#283"{OptimizationFunction{true, Optimization.AutoZygote, var"#15#16", Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, SciMLBase.NullParameters}})(res::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(layer_1 = ViewAxis(1:175, Axis(weight = ViewAxis(1:150, ShapedAxis((25, 6), NamedTuple())), bias = ViewAxis(151:175, ShapedAxis((25, 1), NamedTuple())))), layer_2 = ViewAxis(176:825, Axis(weight = ViewAxis(1:625, ShapedAxis((25, 25), NamedTuple())), bias = ViewAxis(626:650, ShapedAxis((25, 1), NamedTuple())))), layer_3 = ViewAxis(826:1475, Axis(weight = ViewAxis(1:625, ShapedAxis((25, 25), NamedTuple())), bias = ViewAxis(626:650, ShapedAxis((25, 1), NamedTuple())))), layer_4 = ViewAxis(1476:1631, Axis(weight = ViewAxis(1:150, ShapedAxis((6, 25), NamedTuple())), bias = ViewAxis(151:156, ShapedAxis((6, 1), NamedTuple())))))}}}, θ::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(layer_1 = ViewAxis(1:175, Axis(weight = ViewAxis(1:150, ShapedAxis((25, 6), NamedTuple())), bias = ViewAxis(151:175, ShapedAxis((25, 1), NamedTuple())))), layer_2 = ViewAxis(176:825, Axis(weight = ViewAxis(1:625, ShapedAxis((25, 25), NamedTuple())), bias = ViewAxis(626:650, ShapedAxis((25, 1), NamedTuple())))), layer_3 = ViewAxis(826:1475, Axis(weight = ViewAxis(1:625, ShapedAxis((25, 25), NamedTuple())), bias = ViewAxis(626:650, ShapedAxis((25, 1), NamedTuple())))), layer_4 = ViewAxis(1476:1631, Axis(weight = ViewAxis(1:150, ShapedAxis((6, 25), NamedTuple())), bias = ViewAxis(151:156, ShapedAxis((6, 1), NamedTuple())))))}}}, args::Matrix{Float64})
   @ Optimization C:\Users\marco\.julia\packages\Optimization\vFala\src\function\zygote.jl:33
 [4] macro expansion
   @ C:\Users\marco\.julia\packages\OptimizationOptimisers\FWIuf\src\OptimizationOptimisers.jl:31 [inlined]
 [5] macro expansion
   @ C:\Users\marco\.julia\packages\Optimization\vFala\src\utils.jl:37 [inlined]
 [6] __solve(prob::OptimizationProblem{true, OptimizationFunction{true, Optimization.AutoZygote, var"#15#16", Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, ComponentVector{Float64, SubArray{Float64, 1, Vector{Float64}, Tuple{UnitRange{Int64}}, true}, Tuple{Axis{(layer_1 = ViewAxis(1:175, Axis(weight = ViewAxis(1:150, ShapedAxis((25, 6), NamedTuple())), bias = ViewAxis(151:175, ShapedAxis((25, 1), NamedTuple())))), layer_2 = ViewAxis(176:825, Axis(weight = ViewAxis(1:625, ShapedAxis((25, 25), NamedTuple())), bias = ViewAxis(626:650, ShapedAxis((25, 1), NamedTuple())))), layer_3 = ViewAxis(826:1475, Axis(weight = ViewAxis(1:625, ShapedAxis((25, 25), NamedTuple())), bias = ViewAxis(626:650, ShapedAxis((25, 1), NamedTuple())))), layer_4 = ViewAxis(1476:1631, Axis(weight = ViewAxis(1:150, ShapedAxis((6, 25), NamedTuple())), bias = ViewAxis(151:156, ShapedAxis((6, 1), NamedTuple())))))}}}, SciMLBase.NullParameters, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}}, opt::Adam{Float64}, data::IterTools.NCycle{MLUtils.DataLoader{Vector{Matrix{Float64}}, Random._GLOBAL_RNG, Val{nothing}}}; maxiters::Int64, callback::Function, progress::Bool, save_best::Bool, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
   @ OptimizationOptimisers C:\Users\marco\.julia\packages\OptimizationOptimisers\FWIuf\src\OptimizationOptimisers.jl:30
 [7] __solve
   @ C:\Users\marco\.julia\packages\OptimizationOptimisers\FWIuf\src\OptimizationOptimisers.jl:7 [inlined]
 [8] #solve#540
   @ C:\Users\marco\.julia\packages\SciMLBase\QqtZA\src\solve.jl:84 [inlined]
 [9] top-level scope
   @ Untitled-1:98

```

---

<div class="post-metadata">

**Author:** ![abraemer](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/abraemer/32/51403_2.png) [@abraemer](https://discourse.julialang.org/u/abraemer)\
**Post date:** [October 26, 2023, 11:23am UTC](https://discourse.julialang.org/t/uode-error/105429/2 "2023-10-26T11:23:02Z")

</div>

> [@Marco\_Nesta](#):
>
> can anyone help me?

Not with this level of detail I’m afraid.

The error says you try to broadcast something over a dictionary or NamedTuple. How and why that happens we can’t tell without you posting some code. Ideally that code is a bit simplified and can actually be run, because that helps debugging.

---

<div class="post-metadata">

**Author:** ![Marco\_Nesta](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/marco_nesta/32/50318_2.png) [@Marco\_Nesta](https://discourse.julialang.org/u/Marco_Nesta)\
**Post date:** [October 26, 2023, 2:02pm UTC](https://discourse.julialang.org/t/uode-error/105429/3 "2023-10-26T14:02:05Z")

</div>

```plaintext
using Optimization, OptimizationOptimisers, OptimizationOptimJL

using LinearAlgebra, Statistics

using ComponentArrays, Lux, Zygote, Plots, LaTeXStrings, StableRNGs, IterTools
import Flux

rng = StableRNG(1111)

function lotka!(du, u, p, t)
    α, β, γ, δ = [1.3, 0.9, 0.8, 1.8]
    du[1] = α * u[1] - β * u[2] * u[1]
    du[2] = γ * u[1] * u[2] - δ * u[2]
end

function get_train_data()
    t_true = LinRange(0.0,5.0,300)
    tspan = (0.0, 5.0)
    u0 = 5.0f0 * rand(rng, 2)
    prob = ODEProblem(lotka!, u0, tspan, zeros(4))
    solution = solve(prob, Vern7(), abstol = 1e-12, reltol = 1e-12, saveat = t_true);

    Xₙ = Array(solution);
    t = solution.t;

    data = Flux.DataLoader([vcat(Xₙ, t')], batchsize = 1)
    return data
end

train_loader = get_train_data();

rbf(x) = exp.(-(x .^ 2))

# Multilayer FeedForward
U = Lux.Chain( Lux.Dense(6, 25, rbf), 
                Lux.Dense(25, 25, rbf), 
                Lux.Dense(25, 25, rbf),
                Lux.Dense(25, 6))

ps, st = Lux.setup(rng, U)

p_ = zeros(4)

p = ComponentArray{Float64}(p=ps, p_true=p_) 

nn_dynamics! = let
       function nn_dynamics!(du, u, p, t)
              û = U(u, p.p, st)[1] 

              du[1] = p.p_true[1] * u[1] + û[1]
              du[2] = -p.p_true[4] * u[2] + û[2]

              du[3] = p.p_true[1] * u[1] + û[3]
              du[4] = -p.p_true[4] * u[2] + û[4]

              du[5] = p.p_true[1] * u[1] + û[5]
              du[6] = -p.p_true[4] * u[2] + û[6]
       end
end

# Define the problem
prob_nn = ODEProblem(nn_dynamics!, repeat(first(train_loader)[1][1:2,1],3), (0.0, 5.0), p)

function predict(θ, batch)
    T = batch[3, :];  

    IC = batch[1:2, 1];
    
    _prob = remake(prob_nn, u0 = repeat(IC,3), tspan = (T[1], T[end]), p=ComponentArray(p=θ, p_true=[1.3, 0.9, 0.8, 1.8]))
   
    Array(solve(_prob, AutoTsit5(Rodas5()), saveat = T,
                abstol = 1e-6, reltol = 1e-6, verbose=false))
end

function loss(θ, batch)
    X̂ = predict(θ, batch);

    X_true = @view batch[1:2, :]; 

    mean(abs2, X_true .- X̂[1:2,:])
end

losses = Float64[]

callback = function (p, l)
    push!(losses, l)
    if length(losses) % 10 == 0
        println("Current loss after $(length(losses)) iterations: $(losses[end])")
    end
    return false
end

adtype = Optimization.AutoZygote();
optfun = Optimization.OptimizationFunction((θ, p, batch) -> loss(θ, batch), adtype); 
optprob = Optimization.OptimizationProblem(optfun, p.p);

res = Optimization.solve( optprob,
                            ADAM(0.001), 
                            IterTools.ncycle(train_loader, 100),
                            callback = callback)

```

---

<div class="post-metadata">

**Author:** ![abraemer](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/abraemer/32/51403_2.png) [@abraemer](https://discourse.julialang.org/u/abraemer)\
**Post date:** [October 26, 2023, 2:13pm UTC](https://discourse.julialang.org/t/uode-error/105429/4 "2023-10-26T14:13:41Z")

</div>

Ok I am not too familiar with most libraries you use so I can only really guess what happens.  
I suspect the line

```julia
optprob = Optimization.OptimizationProblem(optfun, p.p);

```

and more specifically `p.p` to be responsible for the error. Can you try to pass `ps` directly and see if that fixes it?

```julia
optprob = Optimization.OptimizationProblem(optfun, ps);

```

---

<div class="post-metadata">

**Author:** ![Marco\_Nesta](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/marco_nesta/32/50318_2.png) [@Marco\_Nesta](https://discourse.julialang.org/u/Marco_Nesta)\
**Post date:** [October 26, 2023, 4:22pm UTC](https://discourse.julialang.org/t/uode-error/105429/5 "2023-10-26T16:22:52Z")

</div>

it changes the error

```julia
ERROR: MethodError: no method matching copy(::NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4), NTuple{4, NamedTuple{(:weight, :bias), Tuple{Matrix{Float32}, Matrix{Float32}}}}})

Closest candidates are:
  copy(::Union{DiffEqNoiseProcess.BoxWedgeTail, DiffEqNoiseProcess.NoiseApproximation, DiffEqNoiseProcess.NoiseGrid, DiffEqNoiseProcess.NoiseWrapper, DiffEqNoiseProcess.VirtualBrownianTree})
   @ DiffEqNoiseProcess C:\Users\marco\.julia\packages\DiffEqNoiseProcess\clsTF\src\copy_noise_types.jl:55
  copy(::Union{TransparentColor{C, T}, C} where {T, C<:Union{AbstractRGB{T}, AbstractGray{T}}})
   @ ColorVectorSpace C:\Users\marco\.julia\packages\ColorVectorSpace\QI5vM\src\ColorVectorSpace.jl:250
  copy(::T) where T<:RandomNumbers.Xorshifts.AbstractXoshiro128
   @ RandomNumbers C:\Users\marco\.julia\packages\RandomNumbers\3pD1N\src\Xorshifts\xoshiro128.jl:61
  ...

Stacktrace:
 [1] __solve(prob::OptimizationProblem{true, OptimizationFunction{true, Optimization.AutoZygote, var"#25#26", Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4), NTuple{4, NamedTuple{(:weight, :bias), Tuple{Matrix{Float32}, Matrix{Float32}}}}}, SciMLBase.NullParameters, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}}, opt::Adam{Float64}, data::IterTools.NCycle{MLUtils.DataLoader{Vector{Matrix{Float64}}, Random._GLOBAL_RNG, Val{nothing}}}; maxiters::Int64, callback::Function, progress::Bool, save_best::Bool, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
   @ OptimizationOptimisers C:\Users\marco\.julia\packages\OptimizationOptimisers\FWIuf\src\OptimizationOptimisers.jl:18
 [2] solve(prob::OptimizationProblem{true, OptimizationFunction{true, Optimization.AutoZygote, var"#25#26", Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4), NTuple{4, NamedTuple{(:weight, :bias), Tuple{Matrix{Float32}, Matrix{Float32}}}}}, SciMLBase.NullParameters, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}}, alg::Adam{Float64}, args::IterTools.NCycle{MLUtils.DataLoader{Vector{Matrix{Float64}}, Random._GLOBAL_RNG, Val{nothing}}}; kwargs::Base.Pairs{Symbol, var"#23#24", Tuple{Symbol}, NamedTuple{(:callback,), Tuple{var"#23#24"}}})
   @ SciMLBase C:\Users\marco\.julia\packages\SciMLBase\QqtZA\src\solve.jl:84
 [3] top-level scope
   @ Untitled-1:98

```

---

<div class="post-metadata">

**Author:** ![ChrisRackauckas](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chrisrackauckas/32/77_2.png) [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)\
**Post date:** [October 29, 2023, 1:28pm UTC](https://discourse.julialang.org/t/uode-error/105429/6 "2023-10-29T13:28:39Z")

</div>

```julia
ps = ComponentArray{Float64}(ps) 

```

and then:

```julia
optprob = Optimization.OptimizationProblem(optfun, p.p);

```

---

<div class="post-metadata">

**Author:** ![Marco\_Nesta](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/marco_nesta/32/50318_2.png) [@Marco\_Nesta](https://discourse.julialang.org/u/Marco_Nesta)\
**Post date:** [October 29, 2023, 3:53pm UTC](https://discourse.julialang.org/t/uode-error/105429/7 "2023-10-29T15:53:58Z")

</div>

the error is still there

---

<div class="post-metadata">

**Author:** ![ChrisRackauckas](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chrisrackauckas/32/77_2.png) [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)\
**Post date:** [October 29, 2023, 5:41pm UTC](https://discourse.julialang.org/t/uode-error/105429/8 "2023-10-29T17:41:50Z")

</div>

It looks like it worked fine on the first try?

```julia
using Optimization, OptimizationOptimisers, OptimizationOptimJL

using LinearAlgebra, Statistics

using ComponentArrays, Lux, Zygote, Plots, LaTeXStrings, StableRNGs, IterTools
import Flux

using OrdinaryDiffEq, SciMLSensitivity

rng = StableRNG(1111)

function lotka!(du, u, p, t)
    α, β, γ, δ = [1.3, 0.9, 0.8, 1.8]
    du[1] = α * u[1] - β * u[2] * u[1]
    du[2] = γ * u[1] * u[2] - δ * u[2]
end

function get_train_data()
    t_true = LinRange(0.0,5.0,300)
    tspan = (0.0, 5.0)
    u0 = 5.0f0 * rand(rng, 2)
    prob = ODEProblem(lotka!, u0, tspan, zeros(4))
    solution = solve(prob, Vern7(), abstol = 1e-12, reltol = 1e-12, saveat = t_true);

    Xₙ = Array(solution);
    t = solution.t;

    data = Flux.DataLoader([vcat(Xₙ, t')], batchsize = 1)
    return data
end

train_loader = get_train_data();

rbf(x) = exp.(-(x .^ 2))

# Multilayer FeedForward
U = Lux.Chain( Lux.Dense(6, 25, rbf), 
                Lux.Dense(25, 25, rbf), 
                Lux.Dense(25, 25, rbf),
                Lux.Dense(25, 6))

ps, st = Lux.setup(rng, U)

p_ = zeros(4)

p = ComponentArray{Float64}(p=ps, p_true=p_) 

nn_dynamics! = let
       function nn_dynamics!(du, u, p, t)
              û = U(u, p.p, st)[1] 

              du[1] = p.p_true[1] * u[1] + û[1]
              du[2] = -p.p_true[4] * u[2] + û[2]

              du[3] = p.p_true[1] * u[1] + û[3]
              du[4] = -p.p_true[4] * u[2] + û[4]

              du[5] = p.p_true[1] * u[1] + û[5]
              du[6] = -p.p_true[4] * u[2] + û[6]
       end
end

# Define the problem
prob_nn = ODEProblem(nn_dynamics!, repeat(first(train_loader)[1][1:2,1],3), (0.0, 5.0), p)

function predict(θ, batch)
    T = batch[3, :];  

    IC = batch[1:2, 1];
    
    _prob = remake(prob_nn, u0 = repeat(IC,3), tspan = (T[1], T[end]), p=ComponentArray(p=θ, p_true=[1.3, 0.9, 0.8, 1.8]))
   
    Array(solve(_prob, AutoTsit5(Rodas5()), saveat = T,
                abstol = 1e-6, reltol = 1e-6, verbose=false))
end

function loss(θ, batch)
    X̂ = predict(θ, batch);

    X_true = @view batch[1:2, :]; 

    mean(abs2, X_true .- X̂[1:2,:])
end

losses = Float64[]

callback = function (p, l)
    push!(losses, l)
    if length(losses) % 10 == 0
        println("Current loss after $(length(losses)) iterations: $(losses[end])")
    end
    return false
end

adtype = Optimization.AutoZygote();
optfun = Optimization.OptimizationFunction((θ, p, batch) -> loss(θ, batch), adtype); 
optprob = Optimization.OptimizationProblem(optfun, p.p);

res = Optimization.solve( optprob,
                            ADAM(0.001), 
                            IterTools.ncycle(train_loader, 100),
                            callback = callback)

```