\begin{table}%t3
\caption{Summary of the regularization results from neural networks with different training sets and weight decay term~$\gamma$. Increasing $\gamma$~results in smaller weights of the
network and thus a more regularized solution. However, too large a value of~$\gamma$ will again result in larger deviations, i.e. there is a trade-off in setting this parameter. The metallicity deviations for the standard stars are given in terms of the median values of the
difference (computed value~$-$ literature value). It can be seen that training on noise-free data and validating on noisy data systematically underestimated metallicities. The
results demonstrate that noise in the network inputs can help improve the regularization.}\label{nettab}
 %\centering
\par
\begin{tabular}{ccc}
\hline \hline
Noise in training set & $\gamma$ & \feh\ offset \\
\hline
no & 0.0001 & $-$0.16~~ \\ 
no & 0.001 & $-$0.11~~ \\ 
no & 0.01 & $-$0.07~~ \\ 
yes & 0.0001 & 0.18 \\ 
yes & 0.001 & 0.02 \\ 
yes & 0.01 & 0.05 \\ 
\hline
\end{tabular}
\end{table}