# Flux LSTM format of input train data

**URL:** https://discourse.julialang.org/t/flux-lstm-format-of-input-train-data/37187
**Category:** General Usage
**Tags:** question, data, flux
**Created:** [April 7, 2020, 7:10pm UTC](https://discourse.julialang.org/t/flux-lstm-format-of-input-train-data/37187 "2020-04-07T19:10:14Z")
**Posts on this page:** 1
**Page:** 1

<div class="post-metadata">

### Author: ![Owolf](https://avatars.discourse-cdn.com/v4/letter/o/bbce88/32.png) [@Owolf](https://discourse.julialang.org/u/Owolf)
#### Post date: [April 7, 2020, 7:10pm UTC](https://discourse.julialang.org/t/flux-lstm-format-of-input-train-data/37187/1 "2020-04-07T19:10:15Z")

</div>

Hello community,

I am trying to create easy example using LSTM model. The idea is that model should predict sinus signal. I expected that it is really easy task and it will be done in second. However I am still not able to find good way that would do at least something meaningful. Here is what I recently tried:

```julia
train_data = [sin.(0+i:0.1:1+i) for i in 0:num_samples]
train_labels = [sin.(0+i+1:0.1:1+i+1) for i in 0:num_samples]

m = Chain(LSTM(1,16), LSTM(16,1))

function loss(x, y)
    out = sum(Flux.mse.(m.(x), y))
    Flux.reset!(m)
    out
end

ps = params(m)

function evalcb()
     @show(sum(loss.(train_data, train_labels)))
 end

opt = ADAM();

@epochs 100 Flux.train!(loss, ps, zip(train_data, train_labels), opt, cb = Flux.throttle(evalcb, 60))

```

This will make a model that is not able to predict sinus function 😃

However I would like to firstly ask if I use good input data? Is it type Array{Array{Float64,1},1} correct? Is it correct way how to create a sequence of data?

The second things is when I call a model like:

```julia
m.(train_data[1])

```

Why it returns Array{Array{Float64,2},1}? I mean why LSTM returns 2 dimensional array?

I have already went through lot of information about LSTM but practical use in Julia is something I do not understand at all.

After all, non - reccurent networks works pretty good in Flux 🙂

I will be really happy if somebody can give me some hint what to do.

Thank you.