# Flux Conv layer method error

**URL:** <https://discourse.julialang.org/t/flux-conv-layer-method-error/92937>\
**Category:** New to Julia\
**Tags:** question, images, flux, convolution\
**Created:** [January 13, 2023, 9:27pm UTC](https://discourse.julialang.org/t/flux-conv-layer-method-error/92937 "2023-01-13T21:27:22Z")\
**Posts on this page:** 5\
**Page:** 1

<div class="post-metadata">

**Author:** ![zeio](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/zeio/32/45847_2.png) [@zeio](https://discourse.julialang.org/u/zeio)\
**Post date:** [January 13, 2023, 9:27pm UTC](https://discourse.julialang.org/t/flux-conv-layer-method-error/92937/1 "2023-01-13T21:27:22Z")

</div>

Hi, I am trying to implement a simple train loop in julia to process images, but can’t make my model work. I create the model using the following code:

```julia
model = Chain(
    Conv((3, 3), 3 => 32, relu; pad = (1, 1), bias = false),
    flatten,
    Dense(15488, max_rating),
    softmax
)

```

I’ve tried to delete some parameters passed to the `conv` layer, but in any case it just inserts default values and the situation is essentially the same. The model is created correctly, but when i start training through `Flux.train!` I obtain a method error:

```julia
ERROR: LoadError: MethodError: no method matching (::Conv{2, 2, typeof(relu), Array{Float32, 4}, Bool})(::N0f8)

Closest candidates are:
  (::Conv)(::AbstractArray)
   @ Flux ~/.julia/packages/Flux/v79Am/src/layers/conv.jl:197

Stacktrace:
  [1] macro expansion
    @ ~/.julia/packages/Zygote/AS0Go/src/compiler/interface2.jl:0 [inlined]
  [2] _pullback(ctx::Zygote.Context{true}, f::Conv{2, 2, typeof(relu), Array{Float32, 4}, Bool}, args::N0f8)
    @ Zygote ~/.julia/packages/Zygote/AS0Go/src/compiler/interface2.jl:9
  [3] macro expansion
    @ ~/.julia/packages/Flux/v79Am/src/layers/basic.jl:53 [inlined]
  [4] _pullback
    @ ~/.julia/packages/Flux/v79Am/src/layers/basic.jl:53 [inlined]
  [5] _pullback(::Zygote.Context{true}, ::typeof(Flux._applychain), ::Tuple{Conv{2, 2, typeof(relu), Array{Float32, 4}, Bool}, typeof(flatten), Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, typeof(softmax)}, ::N0f8)
    @ Zygote ~/.julia/packages/Zygote/AS0Go/src/compiler/interface2.jl:0
  [6] _pullback
    @ ~/.julia/packages/Flux/v79Am/src/layers/basic.jl:51 [inlined]
  [7] _pullback(ctx::Zygote.Context{true}, f::Chain{Tuple{Conv{2, 2, typeof(relu), Array{Float32, 4}, Bool}, typeof(flatten), Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, typeof(softmax)}}, args::N0f8)
    @ Zygote ~/.julia/packages/Zygote/AS0Go/src/compiler/interface2.jl:0
  [8] _pullback
    @ ~/posterior/posterior/train.jl:65 [inlined]
  [9] _pullback(ctx::Zygote.Context{true}, f::typeof(L), args::Base.ReinterpretArray{N0f8, 3, RGB{N0f8}, Matrix{RGB{N0f8}}, true})
    @ Zygote ~/.julia/packages/Zygote/AS0Go/src/compiler/interface2.jl:0
 [10] _apply(::Function, ::Vararg{Any})
    @ Core ./boot.jl:838
 [11] adjoint
    @ ~/.julia/packages/Zygote/AS0Go/src/lib/lib.jl:203 [inlined]
 [12] _pullback
    @ ~/.julia/packages/ZygoteRules/AIbCs/src/adjoint.jl:65 [inlined]
 [13] _pullback
    @ ~/.julia/packages/Flux/v79Am/src/optimise/train.jl:143 [inlined]
 [14] _pullback(::Zygote.Context{true}, ::Flux.Optimise.var"#37#40"{typeof(L), Base.ReinterpretArray{N0f8, 3, RGB{N0f8}, Matrix{RGB{N0f8}}, true}})
    @ Zygote ~/.julia/packages/Zygote/AS0Go/src/compiler/interface2.jl:0
 [15] pullback(f::Function, ps::Zygote.Params{Zygote.Buffer{Any, Vector{Any}}})
    @ Zygote ~/.julia/packages/Zygote/AS0Go/src/compiler/interface.jl:384
 [16] withgradient(f::Function, args::Zygote.Params{Zygote.Buffer{Any, Vector{Any}}})
    @ Zygote ~/.julia/packages/Zygote/AS0Go/src/compiler/interface.jl:132
 [17] macro expansion
    @ ~/.julia/packages/Flux/v79Am/src/optimise/train.jl:142 [inlined]
 [18] macro expansion
    @ ~/.julia/packages/ProgressLogging/6KXlp/src/ProgressLogging.jl:328 [inlined]
 [19] train!(loss::Function, ps::Zygote.Params{Zygote.Buffer{Any, Vector{Any}}}, data::Vector{Base.ReinterpretArray{N0f8, 3, RGB{N0f8}, Matrix{RGB{N0f8}}, true}}, opt::Adam; cb::Flux.var"#throttled#124"{Flux.var"#throttled#120#125"{Bool, Bool, typeof(update_loss!), Int64}})
    @ Flux.Optimise ~/.julia/packages/Flux/v79Am/src/optimise/train.jl:140
 [20] kwcall(::NamedTuple{(:cb,), Tuple{Flux.var"#throttled#124"{Flux.var"#throttled#120#125"{Bool, Bool, typeof(update_loss!), Int64}}}}, ::typeof(Flux.Optimise.train!), loss::Function, ps::Zygote.Params{Zygote.Buffer{Any, Vector{Any}}}, data::Vector{Base.ReinterpretArray{N0f8, 3, RGB{N0f8}, Matrix{RGB{N0f8}}, true}}, opt::Adam)
    @ Flux.Optimise ~/.julia/packages/Flux/v79Am/src/optimise/train.jl:136
 [21] top-level scope
    @ ~/posterior/posterior/train.jl:77
in expression starting at /home/zeio/posterior/posterior/train.jl:75

```

Could anybody please help with that?

---

<div class="post-metadata">

**Author:** ![contradict](https://avatars.discourse-cdn.com/v4/letter/c/ac91a4/32.png) [@contradict](https://discourse.julialang.org/u/contradict)\
**Post date:** [January 13, 2023, 9:38pm UTC](https://discourse.julialang.org/t/flux-conv-layer-method-error/92937/2 "2023-01-13T21:38:15Z")

</div>

The error message indicates you are attempting to apply a `conv` operation to a single `N0f8` value rather than an array of them. Would you mind showing your loss function implementation? Perhaps there is an extra broadcast operation.

---

<div class="post-metadata">

**Author:** ![zeio](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/zeio/32/45847_2.png) [@zeio](https://discourse.julialang.org/u/zeio)\
**Post date:** [January 13, 2023, 10:00pm UTC](https://discourse.julialang.org/t/flux-conv-layer-method-error/92937/3 "2023-01-13T22:00:39Z")

</div>

Thanks! It seems that the problem is indeed in the input data. I was passing to the model list of images instead of list of batches. But now there is another error. That’s how I load the input data which are 256x256 images:

```julia
posters = (movie -> get_poster_local_path(movie, "assets/posters/resized") |> load |> channelview).(movies)

```

Then if I view the shape of results via `println(size(posters[1]))` it outputs `(3, 256, 256)`, which seems to be fine. That’s how I then create minibatches following [this tutorial](https://datasciencejuliahackers.com/image-classification.html):

```julia
function make_minibatch(X, Y, indices)
    X_batch = Array{Float32}(undef, size(X[1])..., length(indices))

    for i in 1:length(indices)
        X_batch[:, :, :, i] = Float32.(X[indices[i]])
    end

    Y_batch = onehotbatch(Y[indices], 1:max_rating)

    return (X_batch, Y_batch)
end

```

But model complains about the input data:

```julia
ERROR: LoadError: DimensionMismatch: Input channels must match! (256 vs. 3)

```

Should I somehow transpose my images before passing to model, or there is an issue with model configuration?

---

<div class="post-metadata">

**Author:** ![contradict](https://avatars.discourse-cdn.com/v4/letter/c/ac91a4/32.png) [@contradict](https://discourse.julialang.org/u/contradict)\
**Post date:** [January 13, 2023, 11:20pm UTC](https://discourse.julialang.org/t/flux-conv-layer-method-error/92937/4 "2023-01-13T23:20:45Z")

</div>

Yeah, the tutorial doesn’t have that problem because they convert the images to single-channel (grey) images. The image was loaded as (channel, Y, X), you need to `permutedims(img, (2,3,1))` to move the channel axis to the end.

---

<div class="post-metadata">

**Author:** ![zeio](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/zeio/32/45847_2.png) [@zeio](https://discourse.julialang.org/u/zeio)\
**Post date:** [January 16, 2023, 9:46pm UTC](https://discourse.julialang.org/t/flux-conv-layer-method-error/92937/5 "2023-01-16T21:46:03Z")

</div>

Yes, thank you! I’ve added this and also a couple of other changes to the training loop from [documentation](https://fluxml.ai/Flux.jl/stable/training/training/) and now it works.