# Errors with Flux RNN set

**URL:** <https://discourse.julialang.org/t/errors-with-flux-rnn-set/79272>\
**Category:** Machine Learning\
**Tags:** question, flux, machine-learning\
**Created:** [April 9, 2022, 7:37pm UTC](https://discourse.julialang.org/t/errors-with-flux-rnn-set/79272 "2022-04-09T19:37:00Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![stmorg](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/stmorg/32/35344_2.png) [@stmorg](https://discourse.julialang.org/u/stmorg)\
**Post date:** [April 9, 2022, 7:37pm UTC](https://discourse.julialang.org/t/errors-with-flux-rnn-set/79272/1 "2022-04-09T19:37:00Z")

</div>

Hi everyone, I’m currently working on a final project that involves developing an RNN to represent a parameterized dynamic model. I have been able to do this fairly easily with a feedforward ANN in Flux but I have had major issues when trying to develop a recurrent model.

Before developing the actual model, I have been playing around with a simple model to try to get something to work before I move to actually start the project. My data has 5 features, 1 target, and a sequence length of 20. I generate the data myself so I have full control over the number of batches.

My current setup is as follows:

```julia
n_feat = 5;
n_batches = 10; % 10 batches arbitrarily chosen for test model
seq_len = 20; 
n_targ =1;

m = Chain(RNN(n_feat, n_hidden), Dense(n_hidden, n_targ))  

function loss(x, y)
     Flux.reset!(m)
     sum(mse(m(xi), yi) for (xi, yi) in zip(x, y))
end

x = [rand(Float32, n_feat, n_batches) for i = 1:seq_len] % random data used to build test model
y = [rand(Float32, n_targ, n_batches) for i = 1:seq_len]
data = zip(x,y)

Flux.reset!(m)
ps = params(m)
opt= ADAM(1e-3)
Flux.train!(loss, ps, data, opt) 

```

When I run this I get the following error:

```julia
MethodError: no method matching (::Flux.RNNCell{typeof(tanh), Matrix{Float32}, Vector{Float32}, Matrix{Float32}})(::Matrix{Float32}, ::Vector{Matrix{Float32}}) 

Closest candidates are: (::Flux.RNNCell{F, A, V, var"#s263"} where var"#s263"<:AbstractMatrix{T})(::Any, ::Union{AbstractMatrix{T}, AbstractVector{T}, Flux.OneHotArray}) where {F, A, V, T} 

```

I am not sure how to proceed from here. My intuition tells me I probably am somehow preparing the data wrong such that the type is not what train! expects. Any guidance would be greatly appreciated!

---

<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:** [April 9, 2022, 9:42pm UTC](https://discourse.julialang.org/t/errors-with-flux-rnn-set/79272/2 "2022-04-09T21:42:08Z")

</div>

The comment character should be a `#` not a `%`.

You didn’t define `n_hidden`, I added `n_hidden=10` to test.

Your loss function expects to be passed the entire dataset at once, you can call `loss(x, y)` and get a value. `Flux.train` iterates over the value provided for `data` and calls either `loss(d)` (or `loss(d...)` if d is a tuple) with each element. With the data shapes in your example, that results in passing scalars to your model since you have now zipped twice (once creating `data` and once inside `loss`). I suspect what you meant to do is:

```julia
function loss(x, y)
     Flux.reset!(m)
     Flux.mse(m(x), y)
end

```

Which works as expected in the `train!` loop.

You can test this with

```julia
loss(first(zip(x, y))...)

```