# Neural ODE: MethodError: no method matching copyto!

**URL:** <https://discourse.julialang.org/t/neural-ode-methoderror-no-method-matching-copyto/59306>\
**Category:** New to Julia\
**Created:** [April 14, 2021, 9:40pm UTC](https://discourse.julialang.org/t/neural-ode-methoderror-no-method-matching-copyto/59306 "2021-04-14T21:40:47Z")\
**Posts on this page:** 4\
**Page:** 1

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**Author:** ![Shivam](https://avatars.discourse-cdn.com/v4/letter/s/ce73a5/32.png) [@Shivam](https://discourse.julialang.org/u/Shivam)\
**Post date:** [April 14, 2021, 9:40pm UTC](https://discourse.julialang.org/t/neural-ode-methoderror-no-method-matching-copyto/59306/1 "2021-04-14T21:40:47Z")

</div>

Hello,

I have been trying to implement the neural ODE code example in the [blogpost](https://julialang.org/blog/2019/01/fluxdiffeq/) for a random 1 dimensional array data. But I am getting the error InexactError: Int64(-1.8308279662274551)

If I switch to abs instead of abs2, I get MethodError: no method matching copyto!

I haven’t been able to figure out what’s happening.  
The code in the post is working for me fine. If anyone can point to what is happening, it would be helpful.

v1.6

**EDIT:** The following suggested change by Chris remove the Inexact error but reduce to the other mentioned error.

Code:

```julia

using Flux, DiffEqFlux, DifferentialEquations, Plots

data = rand(10)

t = range(1.0,10.0,length=10)
tspan = (1.0,10.0)
NN = Chain(x-> [x], Dense(1,32,tanh), Dense(32,32,tanh), Dense(32,1), first)
p = Flux.params(NN)
n_ode = NeuralODE(NN,tspan,Tsit5(),saveat=t,reltol=1e-7,abstol=1e-9) 
	
ps = Flux.params(n_ode)

function predict_n_ode()
  Array(n_ode(0.0))
end

loss_n_ode() = sum(abs,data .- predict_n_ode())

opt = ADAM(0.1)
Flux.train!(loss_n_ode,ps,Iterators.repeated((), 1000), opt)

```

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<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:** [April 14, 2021, 10:53pm UTC](https://discourse.julialang.org/t/neural-ode-methoderror-no-method-matching-copyto/59306/2 "2021-04-14T22:53:49Z")

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> [@Shivam](#):
>
> `Array(n_ode(0))`

Array(n\_ode(0.0))

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<div class="post-metadata">

**Author:** ![Shivam](https://avatars.discourse-cdn.com/v4/letter/s/ce73a5/32.png) [@Shivam](https://discourse.julialang.org/u/Shivam)\
**Post date:** [April 14, 2021, 11:16pm UTC](https://discourse.julialang.org/t/neural-ode-methoderror-no-method-matching-copyto/59306/3 "2021-04-14T23:16:44Z")

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Hi!

Thank you very much for the reply. I did try that after posting and it reduce to the other error  
MethodError: no method matching copyto!(::Float64, ::Base.Broadcast  
I rechecked again after your reply to make sure but still that.

Any directions?

Btw, big fan of your SciML lectures. Thank you very much for posting them. I stumbled on them about 15 days ago, heard about Julia for the first time and I am a convert from then on!

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<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:** [April 17, 2021, 11:03am UTC](https://discourse.julialang.org/t/neural-ode-methoderror-no-method-matching-copyto/59306/4 "2021-04-17T11:03:25Z")

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The problem is you weren’t using 1-dimensional arrays but instead scalars as your state. Flux.jl doesn’t do very well with that. This is a a working code:

```julia
using Flux, DiffEqFlux, DifferentialEquations, Plots

data = rand(10)
t = range(1.0,10.0,length=10)
tspan = (1.0,10.0)
NN = Chain(Dense(1,32,tanh), Dense(32,32,tanh), Dense(32,1))
p = Flux.params(NN)
n_ode = NeuralODE(NN,tspan,Tsit5(),saveat=t,reltol=1e-7,abstol=1e-9)

ps = Flux.params(n_ode)

function predict_n_ode()
  Array(n_ode([0.0]))
end

loss_n_ode() = sum(abs,data .- vec(predict_n_ode()))

opt = ADAM(0.1)
Flux.train!(loss_n_ode,ps,Iterators.repeated((), 1000), opt)

```

I would recommend using `FastDense` instead here.