After trying some optimizations on activation function and epochs value , it is not possible to fit the model to y data which is a function of the input data.

```
using Flux, Plots, Statistics
x = Array{Float64}(rand(5, 100));
w = [diff(x[1,:]); 0]./x[1,:];
y1 = cumsum(cos.(cumsum(w)));
scatter(y1)
y = reshape(y1, (1, 100));
data = [(x, y)];
```

```
model = Chain(Dense(5 => 100), Dense(100 => 1), identity)
model[1].weight;
```

```
loss(m, x, y) = Flux.mse(m(x), y)
Flux.mse(model(x), y)
Flux.mse(model(x), y) == mean((model(x) .- y).^2)
opt_stat = Flux.setup(ADAM(), model)
```

```
loss_history = []
epochs = 10000
for epoch in 1:epochs
Flux.train!(loss, model, data, opt_stat)
# print report
train_loss = Flux.mse(model(x), y)
push!(loss_history, train_loss)
println("Epoch = $epoch : Training Loss = $train_loss")
end
```

```
ŷ = model(x)
Flux.mse(model(x), y)
Y = reshape(ŷ, (100, 1));
scatter(Y)
```