# Error in Fitting a Straight Line Tutorial
**URL:** https://discourse.julialang.org/t/error-in-fitting-a-straight-line-tutorial/94591
**Category:** New to Julia
**Tags:** question, flux, zygote
**Created:** [February 13, 2023, 10:19pm UTC](https://discourse.julialang.org/t/error-in-fitting-a-straight-line-tutorial/94591 "2023-02-13T22:19:13Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@gekkoe](https://discourse.julialang.org/u/gekkoe)
#### Post date: [February 13, 2023, 10:19pm UTC](https://discourse.julialang.org/t/error-in-fitting-a-straight-line-tutorial/94591/1 "2023-02-13T22:19:13Z")
I apologize if this has been covered before. I did some searching but didn’t find another mention of it.
I’ve been trying to work through the [Fitting a Straight Line](https://fluxml.ai/Flux.jl/stable/models/overview/#) tutorial on the fluxml.ai site and keep running into an error.
When I get to the part that calls for the line:
```julia
train!(loss, predict, data, opt)
```
I get the following error:
> ERROR: Mutating arrays is not supported – called copyto!(Matrix{Float32}, …)
> This error occurs when you ask Zygote to differentiate operations that change
> the elements of arrays in place (e.g. setting values with x .= …)
>
> Possible fixes:
>
> - avoid mutating operations (preferred)
> - or read the documentation and solutions for this error
> [Limitations · Zygote](https://fluxml.ai/Zygote.jl/latest/limitations)
>
> Stacktrace:
> [1] error(s::String)
> @ Base ./error.jl:35
> [2] \_throw\_mutation\_error(f::Function, args::Matrix{Float32})
> @ Zygote ~/.julia/packages/Zygote/g2w9o/src/lib/array.jl:86
> [3] (::Zygote.var"#395#396"{Matrix{Float32}})(#unused#::Matrix{Float32})
> @ Zygote ~/.julia/packages/Zygote/g2w9o/src/lib/array.jl:101
> [4] (::Zygote.var"#2498#back#397"{Zygote.var"#395#396"{Matrix{Float32}}})(Δ::Matrix{Float32})
> @ Zygote ~/.julia/packages/ZygoteRules/AIbCs/src/adjoint.jl:67
> [5] Pullback
> @ ./broadcast.jl:871 [inlined]
> [6] Pullback
> @ ./broadcast.jl:868 [inlined]
> [7] Pullback
> @ ./broadcast.jl:864 [inlined]
> [8] Pullback
> @ ~/projects/julia/LearnFlux/src/LearnFlux.jl:17 [inlined]
> [9] (::typeof(∂(loss)))(Δ::Float32)
> @ Zygote ~/.julia/packages/Zygote/g2w9o/src/compiler/interface2.jl:0
> [10] (::Zygote.var"#208#209"{Tuple{Tuple{Nothing}, Tuple{Nothing, Nothing}}, typeof(∂(loss))})(Δ::Float32)
> @ Zygote ~/.julia/packages/Zygote/g2w9o/src/lib/lib.jl:206
> [11] (::Zygote.var"#2066#back#210"{Zygote.var"#208#209"{Tuple{Tuple{Nothing}, Tuple{Nothing, Nothing}}, typeof(∂(loss))}})(Δ::Float32)
> @ Zygote ~/.julia/packages/ZygoteRules/AIbCs/src/adjoint.jl:67
> [12] Pullback
> @ ~/.julia/packages/Flux/OxB4x/src/train.jl:107 [inlined]
> [13] (::typeof(∂(λ)))(Δ::Float32)
> @ Zygote ~/.julia/packages/Zygote/g2w9o/src/compiler/interface2.jl:0
> [14] (::Zygote.var"#60#61"{typeof(∂(λ))})(Δ::Float32)
> @ Zygote ~/.julia/packages/Zygote/g2w9o/src/compiler/interface.jl:45
> [15] withgradient(f::Function, args::Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}})
> @ Zygote ~/.julia/packages/Zygote/g2w9o/src/compiler/interface.jl:133
> [16] macro expansion
> @ ~/.julia/packages/Flux/OxB4x/src/train.jl:107 [inlined]
> [17] macro expansion
> @ ~/.julia/packages/ProgressLogging/6KXlp/src/ProgressLogging.jl:328 [inlined]
> [18] train!(loss::Function, model::Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, data::Vector{Tuple{Matrix{Int64}, Matrix{Int64}}}, opt::NamedTuple{(:weight, :bias, :σ), Tuple{Optimisers.Leaf{Optimisers.Descent{Float64}, Nothing}, Optimisers.Leaf{Optimisers.Descent{Float64}, Nothing}, Tuple{}}}; cb::Nothing)
> @ Flux.Train ~/.julia/packages/Flux/OxB4x/src/train.jl:105
> [19] train!(loss::Function, model::Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, data::Vector{Tuple{Matrix{Int64}, Matrix{Int64}}}, rule::Optimisers.Descent{Float64}; cb::Nothing)
> @ Flux.Train ~/.julia/packages/Flux/OxB4x/src/train.jl:118
> [20] train!(loss::Function, model::Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, data::Vector{Tuple{Matrix{Int64}, Matrix{Int64}}}, opt::Flux.Optimise.Descent; cb::Nothing)
> @ Flux ~/.julia/packages/Flux/OxB4x/src/deprecations.jl:124
> [21] train!(loss::Function, model::Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, data::Vector{Tuple{Matrix{Int64}, Matrix{Int64}}}, opt::Flux.Optimise.Descent)
> @ Flux ~/.julia/packages/Flux/OxB4x/src/deprecations.jl:124
> [22] top-level scope
> @ REPL[11]:1
Is this just a case where the tutorial hasn’t been updated to reflect some new change?
The first time through I typed the text, but just to be sure I copy and pasted it on the second and got the same results.
Here is enough code to get to the point where the error occurs:
```julia
using Flux
using Flux: train!
using Statistics
actual(x) = 4x + 2
x_train, x_test = hcat(0:5...), hcat(6:10...)
y_train, y_test = actual.(x_train), actual.(x_test)
model = Dense(1 => 1)
loss(model, x, y) = mean(abs2.(model(x) .= y))
predict = Dense(1 => 1)
data = [(x_train, y_train)]
opt = Descent()
train!(loss, predict, data, opt)
```
Any insight would be much appreciated.
---
### Author:  [@gekkoe](https://discourse.julialang.org/u/gekkoe)
#### Post date: [February 14, 2023, 3:45am UTC](https://discourse.julialang.org/t/error-in-fitting-a-straight-line-tutorial/94591/2 "2023-02-14T03:45:03Z")
Well that’s embarrassing. 🥲
Upon further examination I found that I had a typo and somehow didn’t end up cutting and pasting that line when I re-did the code I’d written by hand.
The line
```julia
loss(model, x, y) = mean(abs2.(model(x) .= y))
```
should have read
```julia
loss(model, x, y) = mean(abs2.(model(x) .- y))
```
Then it works just fine. Decades later and I can still get caught by a punctuation error once in a while. 😅