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:

```
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:

```
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!