# Parameter estimation DefaultOptimizationCache not defined (DiffEqFlux)

**URL:** <https://discourse.julialang.org/t/parameter-estimation-defaultoptimizationcache-not-defined-diffeqflux/98635>\
**Category:** General Usage\
**Tags:** optim, diffeqflux\
**Created:** [May 10, 2023, 6:12pm UTC](https://discourse.julialang.org/t/parameter-estimation-defaultoptimizationcache-not-defined-diffeqflux/98635 "2023-05-10T18:12:36Z")\
**Posts on this page:** 6\
**Page:** 1

<div class="post-metadata">

**Author:** ![ANasc](https://avatars.discourse-cdn.com/v4/letter/a/41988e/32.png) [@ANasc](https://discourse.julialang.org/u/ANasc)\
**Post date:** [May 10, 2023, 6:12pm UTC](https://discourse.julialang.org/t/parameter-estimation-defaultoptimizationcache-not-defined-diffeqflux/98635/1 "2023-05-10T18:12:36Z")

</div>

Hi,

I’m trying to learn the basics of parameter estimation with DiffEqFlux by adapting the first example from here [https://docs.juliahub.com/DiffEqFlux/BdO4p/1.9.0/](https://docs.juliahub.com/DiffEqFlux/BdO4p/1.9.0/) to my own problem. However, I keep getting the following error: `UndefVarError: DefaultOptimizationCache not defined`. This occurs even when I copy and paste the example directly. It seems to be working fine until the last iteration when the error appears. The example is a couple years old, but it’s useful for my purposes. I would greatly appreciate if anyone can tell me what’s going on here and how to fix it. Thanks.

For convenience, here is the code from the example:

```julia
using DifferentialEquations, Flux, Optim, DiffEqFlux
function lotka_volterra(du,u,p,t)
    x, y = u
    α, β, δ, γ = p
    du[1] = dx = α*x - β*x*y
    du[2] = dy = -δ*y + γ*x*y
  end
  u0 = [1.0,1.0]
  tspan = (0.0,10.0)
  p = [1.5,1.0,3.0,1.0]
  prob = ODEProblem(lotka_volterra,u0,tspan,p)
  sol = solve(prob,Tsit5())

  plot(sol)

  function predict_adjoint(p) # Our 1-layer neural network
    Array(concrete_solve(prob,Tsit5(),u0,p,saveat=0.0:0.1:10.0))
  end

  function loss_adjoint(p)
    prediction = predict_adjoint(p)
    loss = sum(abs2,x-1 for x in prediction)
    loss,prediction
  end

  cb = function (p,l,pred) #callback function to observe training
    display(l)
    # using `remake` to re-create our `prob` with current parameters `p`
    display(plot(solve(remake(prob,p=p),Tsit5(),saveat=0.0:0.1:10.0),ylim=(0,6)))
    return false # Tell it to not halt the optimization. If return true, then optimization stops
  end
  
  # Display the ODE with the initial parameter values.
  cb(p,loss_adjoint(p)...)
  
  res = DiffEqFlux.sciml_train(loss_adjoint, p, BFGS(initial_stepnorm = 0.0001), cb = cb)[1]

  plot(solve(remake(prob,p=res.minimizer),Tsit5(),saveat=0.0:0.1:10.0),ylim=(0,6))

And the stacktrace:
ERROR: UndefVarError: DefaultOptimizationCache not defined
Stacktrace:
 [1] ___solve(prob::OptimizationProblem{true, OptimizationFunction{true, Optimization.AutoZygote, DiffEqFlux.var"#121#128"{typeof(loss_adjoint)}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}, SciMLBase.NullParameters, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}}, opt::BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Float64, Flat}, data::Base.Iterators.Cycle{Tuple{Optimization.NullData}}; callback::Function, maxiters::Nothing, maxtime::Nothing, abstol::Nothing, reltol::Nothing, progress::Bool, kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
   @ OptimizationOptimJL C:\Users\alexa\.julia\packages\OptimizationOptimJL\WqQOV\src\OptimizationOptimJL.jl:168
 [2] #__solve#2
   @ C:\Users\alexa\.julia\packages\OptimizationOptimJL\WqQOV\src\OptimizationOptimJL.jl:67 [inlined]
 [3] #solve#486
   @ C:\Users\alexa\.julia\packages\SciMLBase\kTnku\src\solve.jl:89 [inlined]
 [4] sciml_train(::typeof(loss_adjoint), ::Vector{Float64}, ::BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Float64, Flat}, ::Nothing; lower_bounds::Nothing, upper_bounds::Nothing, cb::Function, callback::Function, maxiters::Nothing, kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
   @ DiffEqFlux C:\Users\alexa\.julia\packages\DiffEqFlux\2IJEZ\src\train.jl:45
 [5] top-level scope
   @ c:\Users\alexa\Documents\Research\Chapter 2\Production_fit.jl:191
```

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<div class="post-metadata">

**Author:** ![nilshg](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nilshg/32/2283_2.png) [@nilshg](https://discourse.julialang.org/u/nilshg)\
**Post date:** [May 10, 2023, 6:22pm UTC](https://discourse.julialang.org/t/parameter-estimation-defaultoptimizationcache-not-defined-diffeqflux/98635/2 "2023-05-10T18:22:31Z")

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Which version of `DiffEqFlux` are you using? The docs you link are for version 1.9, and the current release is 2.0 (and between that there were quite a few minor versions, the one before that was 1.54.0…)

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<div class="post-metadata">

**Author:** ![ANasc](https://avatars.discourse-cdn.com/v4/letter/a/41988e/32.png) [@ANasc](https://discourse.julialang.org/u/ANasc)\
**Post date:** [May 10, 2023, 6:26pm UTC](https://discourse.julialang.org/t/parameter-estimation-defaultoptimizationcache-not-defined-diffeqflux/98635/3 "2023-05-10T18:26:35Z")

</div>

I’m using v1.53.0

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<div class="post-metadata">

**Author:** ![nilshg](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nilshg/32/2283_2.png) [@nilshg](https://discourse.julialang.org/u/nilshg)\
**Post date:** [May 11, 2023, 8:27am UTC](https://discourse.julialang.org/t/parameter-estimation-defaultoptimizationcache-not-defined-diffeqflux/98635/4 "2023-05-11T08:27:56Z")

</div>

So you should also use the docs for that version, which don’t have the example you posted. Maybe one of the tutorials in the current docs covers what you are after?

[https://docs.sciml.ai/DiffEqFlux/stable/examples/neural\_ode/](https://docs.sciml.ai/DiffEqFlux/stable/examples/neural_ode/)

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<div class="post-metadata">

**Author:** ![ANasc](https://avatars.discourse-cdn.com/v4/letter/a/41988e/32.png) [@ANasc](https://discourse.julialang.org/u/ANasc)\
**Post date:** [May 11, 2023, 6:03pm UTC](https://discourse.julialang.org/t/parameter-estimation-defaultoptimizationcache-not-defined-diffeqflux/98635/5 "2023-05-11T18:03:03Z")

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I just tried the example in your link but I get the same `DefaultOptimizationCache not defined` error when I try to run `result_neuralode = Optimization.solve(...)`. I can see when I plot the callback that the fitting process is working just fine, but it’s just not storing the solution in `result_neuralode`. There must be something wrong on my end but I’m not sure what it could be. I updated all the packages, restarted Julia etc. but to no avail.

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<div class="post-metadata">

**Author:** ![ANasc](https://avatars.discourse-cdn.com/v4/letter/a/41988e/32.png) [@ANasc](https://discourse.julialang.org/u/ANasc)\
**Post date:** [May 15, 2023, 2:25pm UTC](https://discourse.julialang.org/t/parameter-estimation-defaultoptimizationcache-not-defined-diffeqflux/98635/6 "2023-05-15T14:25:58Z")

</div>

Update: I updated SciMLBase and it seems to have resolved the issue.