Thank you!

The Mnist database got classified pretty well

There was just something ‘odd’

I downloaded MNIST from

```
using Flux, Flux.Data.MNIST, Statistics
imgs = MNIST.images()
X = Array(transpose(hcat(float.(reshape.(imgs, :))...) ))
labels = MNIST.labels() .+1
```

The labels go from 0 to 9, but I had to translate them to [1,10] otherwise I got this error.

Looking at the number I had the intuition it was scoring over 1:9, like the class with 0 was ‘not welcome’

For the rest it worked smoothly,

Thanks a lot

```
DimensionMismatch("new dimensions (785, 10) must be consistent with array size 7056")
(::Base.var"#throw_dmrsa#197")(::Tuple{Int64,Int64}, ::Int64) at reshapedarray.jl:41
reshape at reshapedarray.jl:45 [inlined]
reshape at reshapedarray.jl:116 [inlined]
apply_X!(::Array{Float64,2}, ::Array{Float64,2}, ::Array{Float64,1}, ::Int64) at utils.jl:66
(::MLJLinearModels.var"#102#103"{GeneralizedLinearRegression{MultinomialLoss,ScaledPenalty{LPPenalty{2}}},Array{Float64,2},Array{Int64,1},Int64,Int64,Int64,Float64})(::Float64, ::Array{Float64,1}, ::Array{Float64,1}) at d_logistic.jl:149
(::NLSolversBase.var"#61#62"{NLSolversBase.InplaceObjective{Nothing,MLJLinearModels.var"#102#103"{GeneralizedLinearRegression{MultinomialLoss,ScaledPenalty{LPPenalty{2}}},Array{Float64,2},Array{Int64,1},Int64,Int64,Int64,Float64},Nothing,Nothing,Nothing},Float64})(::Array{Float64,1}, ::Array{Float64,1}) at incomplete.jl:45
value_gradient!!(::NLSolversBase.OnceDifferentiable{Float64,Array{Float64,1},Array{Float64,1}}, ::Array{Float64,1}) at interface.jl:82
initial_state(::Optim.LBFGS{Nothing,LineSearches.InitialStatic{Float64},LineSearches.HagerZhang{Float64,Base.RefValue{Bool}},Optim.var"#19#21"}, ::Optim.Options{Float64,Nothing}, ::NLSolversBase.OnceDifferentiable{Float64,Array{Float64,1},Array{Float64,1}}, ::Array{Float64,1}) at l_bfgs.jl:158
optimize(::NLSolversBase.OnceDifferentiable{Float64,Array{Float64,1},Array{Float64,1}}, ::Array{Float64,1}, ::Optim.LBFGS{Nothing,LineSearches.InitialStatic{Float64},LineSearches.HagerZhang{Float64,Base.RefValue{Bool}},Optim.var"#19#21"}, ::Optim.Options{Float64,Nothing}) at optimize.jl:33
#optimize#93 at interface.jl:116 [inlined]
optimize(::NLSolversBase.InplaceObjective{Nothing,MLJLinearModels.var"#102#103"{GeneralizedLinearRegression{MultinomialLoss,ScaledPenalty{LPPenalty{2}}},Array{Float64,2},Array{Int64,1},Int64,Int64,Int64,Float64},Nothing,Nothing,Nothing}, ::Array{Float64,1}, ::Optim.LBFGS{Nothing,LineSearches.InitialStatic{Float64},LineSearches.HagerZhang{Float64,Base.RefValue{Bool}},Optim.var"#19#21"}, ::Optim.Options{Float64,Nothing}) at interface.jl:115
optimize(::NLSolversBase.InplaceObjective{Nothing,MLJLinearModels.var"#102#103"{GeneralizedLinearRegression{MultinomialLoss,ScaledPenalty{LPPenalty{2}}},Array{Float64,2},Array{Int64,1},Int64,Int64,Int64,Float64},Nothing,Nothing,Nothing}, ::Array{Float64,1}, ::Optim.LBFGS{Nothing,LineSearches.InitialStatic{Float64},LineSearches.HagerZhang{Float64,Base.RefValue{Bool}},Optim.var"#19#21"}) at interface.jl:115
_fit(::GeneralizedLinearRegression{MultinomialLoss,ScaledPenalty{LPPenalty{2}}}, ::LBFGS, ::Array{Float64,2}, ::Array{Int64,1}) at newton.jl:114
#fit#144(::LBFGS, ::typeof(fit), ::GeneralizedLinearRegression{MultinomialLoss,ScaledPenalty{LPPenalty{2}}}, ::Array{Float64,2}, ::Array{Int64,1}) at default.jl:48
fit(::GeneralizedLinearRegression{MultinomialLoss,ScaledPenalty{LPPenalty{2}}}, ::Array{Float64,2}, ::Array{Int64,1}) at default.jl:38
top-level scope at test_LR.jl:159
```