\pgfplotsset{
compat=1.11,
legend image code/.code={
\draw[mark repeat=2,mark phase=2]
plot coordinates {
(0cm,0cm)
(0.15cm,0cm)        %% default is (0.3cm,0cm)
(0.3cm,0cm)         %% default is (0.6cm,0cm)
};%
}
}
\begin{figure}
  \begin{subfigure}[h]{\textwidth}
    \begin{tikzpicture}
      \small
      \begin{axis}[legend cell align={left},yticklabel style={/pgf/number format/fixed,
                     /pgf/number format/precision=3},tick style = {draw = none}, width = 0.975\textwidth,
        height = 0.6\textwidth, ymin = 0.988, legend style={at={(0.9825,0.0175)},anchor=south east},
        xlabel = {Epoch}, ylabel = {Test Accuracy}, cycle
        list/Dark2, every axis plot/.append style={line width =1.25pt}]
        \addplot table
        [x=epoch, y=val_accuracy, col sep=comma, mark = none]
        {Figures/Data/adam_datagen_full_mean.log};  
        \addplot table
        [x=epoch, y=val_accuracy, col sep=comma, mark = none]
        {Figures/Data/adam_datagen_dropout_02_full_mean.log}; 
        \addplot table
        [x=epoch, y=val_accuracy, col sep=comma, mark = none]
        {Figures/Data/adam_datagen_dropout_04_full_mean.log};
        \addplot table
        [x=epoch, y=val_accuracy, col sep=comma, mark = none]
        {Figures/Data/adam_dropout_02_full_mean.log}; 
        \addplot table
        [x=epoch, y=val_accuracy, col sep=comma, mark = none]
        {Figures/Data/adam_dropout_04_full_mean.log}; 
        \addplot [dashed] table
        [x=epoch, y=val_accuracy, col sep=comma, mark = none]
        {Figures/Data/adam_full_mean.log}; 
        
        \addlegendentry{\footnotesize{G.}}
        \addlegendentry{\footnotesize{G. + D. 0.2}}
        \addlegendentry{\footnotesize{G. + D. 0.4}}
        \addlegendentry{\footnotesize{D. 0.2}}
        \addlegendentry{\footnotesize{D. 0.4}}
        \addlegendentry{\footnotesize{Default}}
      \end{axis}
    \end{tikzpicture}
    \caption{Test Accuracy}
    \vspace{.25cm}
  \end{subfigure}
  \begin{subfigure}[h]{1.0\linewidth}
    \begin{tabu} to \textwidth {@{}lc*5{X[c]}@{}}
      \Tstrut \Bstrut & Default & D. 0.2 & D. 0.4 & G. &G.+D.\,0.2 & G.+D.\,0.4 \\
      \hline           
      \multicolumn{7}{c}{Test Accuracy}\Bstrut                       \\
      \cline{2-7}
      mean     \Tstrut   & 0.9914 & 0.9923 & 0.9930 & 0.9937 & 0.9943 & 0.9944 \\
      max                & 0.9926 & 0.9930 & 0.9934 & 0.9946 & 0.9957 & 0.9956 \\
      min                & 0.9887 & 0.9909 & 0.9922 & 0.9929 & 0.9930 & 0.9934 \\
      \hline
      \multicolumn{7}{c}{Training Accuracy}\Bstrut                             \\
      \cline{2-7}
      mean       \Tstrut & 0.9994 & 0.9991 & 0.9989 & 0.9967 & 0.9954 & 0.9926 \\
      max                & 0.9996 & 0.9996 & 0.9992 & 0.9979 & 0.9971 & 0.9937 \\
      min                & 0.9992 & 0.9990 & 0.9984 & 0.9947 & 0.9926 & 0.9908 \\      
    \end{tabu}
    \caption{Mean, maximum and minimum accuracy after 50 epochs of training.}
    \label{fig:gen_dropout_b}
  \end{subfigure}
  \caption[Performance Comparison of Overfitting Measures]{Accuracy
    for the net given in Figure~\ref{fig:mnist_architecture} with Dropout (D.),
    data generation (G.), a combination, or neither (Default) implemented and trained
    with \textsc{Adam}. For each epoch the 60.000 training samples
    were used, or for data generation 10.000 steps with each using
    batches of 60 generated data points. For each configuration the
    model was trained five times and the average accuracies at each epoch
    are given in (a). Mean, maximum and minimum values of accuracy on
    the test and training set are given in (b).}
  \label{fig:gen_dropout}
\end{figure}
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