\begin{optiontableAuto}
Architecture & \mc{1}{c}{--} & CPU & CPU, GPU & Compute architecture backend to use for the training of the network \\
ErrorStrategy & \mc{1}{c}{--} & CROSSENTROPY & CROSSENTROPY, SUMOFSQUARES & Loss functions used for the training \\
Layout & \mc{1}{c}{--} & \mc{1}{c}{--} & See Section ~\ref{sec:dnn:layout} & Layout of the network\\
InputLayout & \mc{1}{c}{--} & Number of input features & See Section ~\ref{sec:dnn:inputlayout} & Input shape for first layer. Needed for CNN and RNN \\
TrainingStrategy & \mc{1}{c}{--} & \mc{1}{c}{--} & See Section ~\ref{sec:dnn:training} & Training Settings \\
WeightInitialization & \mc{1}{c}{--} & XAVIER & XAVIER, XAVIERUNIFORM & Weight initialization method
\end{optiontableAuto}