I'm trying to restore this function:
$$ F(x) = x*sin(\alpha x)+b; \space\space \alpha,b \in (-20,20) $$
My NN model(with Keras) is:
1 layer: GRU, 9 neurons, selu activation
2 layer: GRU, 3 neurons, selu activation
1 layer: GRU, 7 neurons, selu activation
1 layer: Dense, 1 neuron, linear activation
kernel init he_normal for all layers
For the training dataset I've generated function values $f(x), x \in -5..5$ (e.g.
linspace(-5,5,500) and random integer values for $\alpha$ and $b$, 250 times.
Then I've selected $f[i-1]$ and $f[i]$ (previous steps) and for this "xs" the output is $f[i+1]$. So the training dataset is like:
first row: previous previous $f$, $\alpha$, $b$
second row: previous $f$, $\alpha$, $b$
Y: current $f$ value
After training over 200 epochs, mae on validation data was 0.1851.
Now if I try to predict new data it seems normal, but when I try to predict new values using previously predicted by model points it falls down and it doesn't looks like sine function at all.
What am I doing wrong?