# Very basic Flux problem

**URL:** <https://discourse.julialang.org/t/very-basic-flux-problem/25663>\
**Category:** Machine Learning\
**Created:** [June 25, 2019, 6:51pm UTC](https://discourse.julialang.org/t/very-basic-flux-problem/25663 "2019-06-25T18:51:59Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![austinbean](https://avatars.discourse-cdn.com/v4/letter/a/839c29/32.png) [@austinbean](https://discourse.julialang.org/u/austinbean)\
**Post date:** [June 25, 2019, 6:51pm UTC](https://discourse.julialang.org/t/very-basic-flux-problem/25663/1 "2019-06-25T18:51:59Z")

</div>

I’m starting to use flux for an NN task, but I’m having trouble understanding an error message. The following is not my use case, but maybe if I understand what’s going wrong here I can fix my actual problem…

```julia

using Flux

model = Dense(21, 10, sigmoid)
loss_f(x,y) = Flux.mse(model(x), y)
opt = ADAM(0.1)

inp_data = rand(21,)
out_labs = rand(1:4, 1)

loss_f(inp_data, out_labs) # this works, e.g., I get a number

Flux.train!(loss_f, Flux.params(model), zip(inp_data, out_labs), opt)

```

at the last line I get:

```julia
ERROR: MethodError: no method matching (::Dense{typeof(σ),TrackedArray{…,Array{Float32,2}},TrackedArray{…,Array{Float32,1}}})(::Float64)
Closest candidates are:
  Dense(::AbstractArray{T<:Union{Float32, Float64},N} where N) where {T<:Union{Float32, Float64}, W<:(AbstractArray{T<:Union{Float32, Float64},N} where N)} at /Users/austinbean/.julia/packages/Flux/qXNjB/src/layers/basic.jl:110
  Dense(::AbstractArray{#s104,N} where N where #s104<:Real) where {T<:Union{Float32, Float64}, W<:(AbstractArray{T<:Union{Float32, Float64},N} where N)} at /Users/austinbean/.julia/packages/Flux/qXNjB/src/layers/basic.jl:113
  Dense(::AbstractArray) at /Users/austinbean/.julia/packages/Flux/qXNjB/src/layers/basic.jl:98
Stacktrace:
 [1] loss_f(::Float64, ::Int64) at ./none:1
 [2] #14 at /Users/austinbean/.julia/packages/Flux/qXNjB/src/optimise/train.jl:72 [inlined]
 [3] gradient_(::getfield(Flux.Optimise, Symbol("##14#20")){typeof(loss_f)}, ::Tracker.Params) at /Users/austinbean/.julia/packages/Tracker/RRYy6/src/back.jl:97
 [4] #gradient#24(::Bool, ::Function, ::Function, ::Tracker.Params) at /Users/austinbean/.julia/packages/Tracker/RRYy6/src/back.jl:164
 [5] gradient at /Users/austinbean/.julia/packages/Tracker/RRYy6/src/back.jl:164 [inlined]
 [6] macro expansion at /Users/austinbean/.julia/packages/Flux/qXNjB/src/optimise/train.jl:71 [inlined]
 [7] macro expansion at /Users/austinbean/.julia/packages/Juno/TfNYn/src/progress.jl:124 [inlined]
 [8] #train!#12(::getfield(Flux.Optimise, Symbol("##16#22")), ::Function, ::Function, ::Tracker.Params, ::Base.Iterators.Zip{Tuple{Array{Float64,1},Array{Int64,1}}}, ::ADAM) at /Users/austinbean/.julia/packages/Flux/qXNjB/src/optimise/train.jl:69
 [9] train!(::Function, ::Tracker.Params, ::Base.Iterators.Zip{Tuple{Array{Float64,1},Array{Int64,1}}}, ::ADAM) at /Users/austinbean/.julia/packages/Flux/qXNjB/src/optimise/train.jl:67
 [10] top-level scope at none:0

```

I confess that I don’t really know what Flux is looking for in the `data` field in `train!()` - the docs are a little terse there. But this may have absolutely nothing to do with the problem. Thanks for your help with such a basic question.

---

<div class="post-metadata">

**Author:** ![dellison](https://avatars.discourse-cdn.com/v4/letter/d/ce73a5/32.png) [@dellison](https://discourse.julialang.org/u/dellison)\
**Post date:** [June 25, 2019, 7:23pm UTC](https://discourse.julialang.org/t/very-basic-flux-problem/25663/2 "2019-06-25T19:23:24Z")

</div>

I don’t have a Julia installation on my work computer, unfortunately, so I can’t totally verify this but I think I see what’s going on. Try changing the `train!` call to this and see if it works:

```julia
Flux.train!(loss_f, Flux.params(model), [(inp_data, out_labs)], opt)

```

When you `zip(inp_data, out_labs)`, you’re having it basically train with the individual floats from the input and output vectors (notice that the error message is complaining that it doesn’t have a method for `Dense` that accepts a single Float64 as input). You can see in [the definition of train!](https://github.com/FluxML/Flux.jl/blob/master/src/optimise/train.jl#L72) that it just iterates over the data and splats each example in the loss function input, pretty much like this:

```julia
for d in data
    loss(d...)
end

```

In other words, if your data looks like this (and your loss function is of the form `loss(x, y)`):

`data = [(x1, y1), (x2, y2), ...]`

then you can pass it in as-is:

`Flux.train!(loss, params(model), data, opt)`

I’m guessing that you picked up the `zip` from an example where you had something like:

```julia
xs = [x1, x2, x3, ...]
ys = [y1, y2, y3, ...]

```

In that case, you can zip them up like this:

`Flux.train!(loss, params(model), zip(xs, ys), opt)`

Hope that helps! 🙂