```
using CSV, DataFrames
using Flux, Statistics, ProgressMeter
using Plots
using CUDA,AMDGPU;
# Read the CSV file into a DataFrame
df = CSV.File("real_values_ex_00_fixed_time_step.csv") |> DataFrame
# Extract the temperature and time steps vectors
Temperatura = df[1:10:end, 2]
tsteps = df[1:10:end, 1]
#normalizze temperatura tra 0 e 1
Temperatura= (Temperatura .- minimum(Temperatura)) ./ (maximum(Temperatura) - minimum(Temperatura))
# Convert vectors into matrices
Temperatura = reshape(Temperatura, 1, :)
tsteps = reshape(tsteps, 1, :)
# Example: Using DataLoader
# Assuming `tsteps` are features and `Temperatura` are labels
loader = Flux.DataLoader((tsteps, Temperatura))
model = Flux.Chain(Flux.Dense(1 => 23, tanh),Flux.Dense(23 => 23,tanh),Flux.Dense(23 => 1, bias=false))
opt_state = Flux.setup(Flux.OptimiserChain(Flux.WeightDecay(0.42), Flux.Adam(0.01)), model)
loss(x,y)= mean(abs2.(model(x) .- y));
for epoch in 1:100
Flux.train!(model, loader, optim) do m, x, y
loss(m(x), y)
end
end
out2 = model(tsteps)
plot(tsteps[1,:], Temperatura[1,:], label="Real values")
plot!(tsteps[1,:], out2[1,:], label="Predicted values")
```

Hi Mak, itâ€™s not clear what you are trying to ask. Maybe take a look here Please read: make it easier to help you

I have temperature and date data and my goal is to train an MLP neural network in flux for future temperature prediction

And the question isâ€¦

I am recently in julia and would like to know how to improve performance, after a while my training reaches a minimum it seems to me

Is there a reason to expect more performance out of this simple network?

We donâ€™t really know what data you have, or how well it performs. It might be easier to give feedback if you provide more details.

Is there a reason you donâ€™t use a recurrent network for this, they are typically good at time-series modelling.

Thatâ€™s good! It means youâ€™ve successfully converged on the optimal parameters.

For a simpler example, you can think of linear regression, where we have a function like

```
output = a * input + b
```

â€śTrainingâ€ť just means looking for the best values of `a`

and `b`

to predict the output. Once weâ€™ve found the correct values, training longer doesnâ€™t do anything, because `a`

and `b`

arenâ€™t changingâ€“any other values of `a`

and `b`

would be worse.