Hyper Parameter Optimization
$$ M(x_1, x_2, x_3, ... , x_n) = \sum_{q=0}^{2n} \Phi_q\left(\sum_{p=1}^{n} \phi_{q,p}(x_{p})\right). $$
At the initial stage, we limit the number of possible options of the model to the following list:
 Number of addends. Single addend not look as scary as entire network
$$\Phi\left(\sum_{p=1}^{n} \phi_{p}(x_{p})\right). $$
We may add and remove addends during the training.
 We express all functions as the same length sequential linear blocks. The optimal number of blocks must be determined in training.
 Parameter of regularization.

