I’m trying to reproduce this example but my model won’t train.

I use only train dataset to achieve overfitting and get just a line as prediction:

```
using Pandas
using Plots
using Flux
df = read_csv("airline-passengers.csv")
# Get the two datasets of one point time-series
# The train data and target
train = values(df[1:length(df)-2].Passengers)
target_train = values(df[2:length(df)].Passengers)
Plots.plot(train)
plot!(target_train)
train = [[convert(Float32, train[i])] for i = 1:length(train)]
train = reshape(train, (length(train), 1))
target_train = reshape(target_train, (length(target_train), 1))
target_train = [convert(Float32, target_train[i]) for i = 1:length(target_train)]
# Model and Training
model = Chain(RNN(1,4), Dense(4, 1, x->x))
loss(x, y) = sum(abs2.(eval_model(x) .- y))
function eval_model(x)
#@show x
out = model.(x)
@show out
#Flux.reset!(model)
out = [out[i][1] for i=1:length(out)]
end
ps = Flux.params(model);
opt = Flux.ADAM(0.01)
epochs = 100
for epoch in 1:epochs
@show epoch
#loss(train, target_train)
gs = Flux.gradient(ps) do
@show loss(train, target_train)
end
Flux.Optimise.update!(opt, ps, gs)
end
out = model.(train)
out = [out[i][1] for i=1:length(out)]
Plots.plot(out)
```

Does anybody see any problem?