\chapter{Results}
In the last chapter we gave simple possibilities to enhance a net for palying GO. We
created some simple net and started training them.
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\textit{Diese Kapitel wird noch weiter ausgearbeitet}
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\begin{table}[htb]
\begin{tabular}{c||c|c|c}
& \textbf{Standard} & \textbf{Standard-Extended} & \textbf{Influence} \\ \hline\hline
\textbf{25 hidden} & 184 & 123 & 930 \\
\textbf{50 hidden} & 160 & 98 & 846 \\
\textbf{150 hidden} & 162 & 86 & 717 \\
\textbf{300 hidden} & 320 & 156 & 555 \\
\end{tabular}
\end{table}

% Beschreibung für Tabelle
SSE after 1000 cycles of training using momentum backpropagation technique with a
training set of 576 patterns and a fully connected hidden layer.
 
% Resume
Further investigation of the neural networks (this time including a third encoding scheme) in
respect to their ability to learn a great variety of different game situations indicate that the
nets benefit from a simple encoding technique and are scaling well regarding the large
training set. Looking at the results it is evident that standard encoding and the extended
version of it are most efficient. Taking into account that standard encoding manages to do
with only half the number of input neurons, it seems reasonable to stick to it, though the
extended variant produces lower error rates.

Considering the hidden layer's size, an amount of about 150 neurons appears to be
functional.


%vorne bei den Kodierungsmöglichkeiten einfügen und evtl. noch ein bißchen ausführen
Standard Extended: similar scheme as standard encoding with the difference being the fact
that only  0 and 1 are valid input values and the game situation is encoded seperately for
black and white stones. The consequence is the input layer being twice as large compared
to the other two designs.



