# The tracked parameters are not updated during Flux.training

**URL:** <https://discourse.julialang.org/t/the-tracked-parameters-are-not-updated-during-flux-training/42507>\
**Category:** General Usage\
**Tags:** diffeq, flux\
**Created:** [July 4, 2020, 3:33am UTC](https://discourse.julialang.org/t/the-tracked-parameters-are-not-updated-during-flux-training/42507 "2020-07-04T03:33:07Z")\
**Posts on this page:** 1\
**Showing post:** 1

<div class="post-metadata">

**Author:** ![yanz4](https://avatars.discourse-cdn.com/v4/letter/y/b5e925/32.png) [@yanz4](https://discourse.julialang.org/u/yanz4)\
**Post date:** [July 4, 2020, 3:33am UTC](https://discourse.julialang.org/t/the-tracked-parameters-are-not-updated-during-flux-training/42507/1 "2020-07-04T03:33:07Z")

</div>

I’m building an oscillator defined by ODE. The purpose is to use Flux training to optimize parameters in ODE, i.e. the R and C value of the oscillator, given loss function.

```julia
using Flux, DifferentialEquations, DiffEqSensitivity

# encapsule all parameters in VO struct
struct VO
    Cth::Any
    Rth::Any
end

Flux.@functor VO
Flux.trainable(device::VO) = (device.Rth, device.Cth)

# VO constructor
VO() = VO([1.63e-13], [4.0e5])

# 2nd order function of VO
function (device::VO)(Input_time_series)
    tspan = (0.0, 1e-5)
    u0 = vcat(repeat([1.0]; outer = [10]), repeat([300.0]; outer = [10]))
    # single device paramters
    p = [device.Cth[1], device.Rth[1]]

    function VO_system!(du, u, p, t)
        Cth, Rth = p
        V = u[1:10]
        T = u[11:end]
        du[1:10] = -V
        @. du[11:end] = 1 / Cth * (V - T / Rth)
    end

    sol = concrete_solve(
        ODEProblem(VO_system!, u0, tspan, p),
        Tsit5();
        u0 = u0,
        p = p,
        saveat = range(tspan[1], tspan[2], length = 500),
        sensealg = QuadratureAdjoint(),
        jac = true,
        abstol = 1e-5,
        reltol = 1e-5,
    )

    return sol
end

###############################################Training#########################

device = VO()

model = device

ps = params(model)

loss(x, y) = Flux.mse(model(x), y)

println("Tracked Parameters: ", ps)

gs = Flux.gradient(() -> loss(0, 0), ps)
@show gs[ps[1]]
@show gs[ps[2]]

```

The problem I met is that, during training, while `Flux.params(model)` confirms that the parameters are already being tracked, these tracked parameters are not updated. I tested the gradient by `gs=Flux.gradient()` , and it returns nothing.  
The output is like below:

> Tracked Parameters: Params([[400000.0], [1.63e-13]])  
> gs[ps[1]] = nothing  
> gs[ps[2]] = nothing

I’d be grateful of any insights into it.

---

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