Figure 13.

A sketch of our two-stage MLP model (top) and an affiliated training procedure (bottom). For an input histogram of size $n=625$ and an output layer size of $m=10$ neurons, the secondary MLP receives the primary MLPs’ output activation plus another histogram as input. In our training procedure, we split our dataset D in three parts $D_1$, $D_2$ and $D_3$, such that we may train our two MLPs on independent parts of the dataset and test it on unseen data.