Masterarbeit/TeX/Plots/pfg_test.tex

\documentclass{article}
\usepackage{pgfplots}
\usepackage{filecontents}
\usepackage{subcaption}
\usepackage{adjustbox}
\usepackage{xcolor}
\usepackage{tabu}
\usepackage{graphicx}
\usetikzlibrary{calc, 3d}

\begin{document}
\pgfplotsset{
compat=1.11,
legend image code/.code={
\draw[mark repeat=2,mark phase=2]
plot coordinates {
(0cm,0cm)
(0.0cm,0cm)        %% default is (0.3cm,0cm)
(0.0cm,0cm)         %% default is (0.6cm,0cm)
};%
}
}
\begin{figure}
  \begin{subfigure}[b]{\textwidth}
    \begin{tikzpicture}
      \begin{axis}[tick style = {draw = none}, width = \textwidth,
        height = 0.7\textwidth,
        xtick = {1, 3, 5,7,9,11,13,15,17,19},
        xticklabels = {$2$, $4$, $6$, $8$,
          $10$,$12$,$14$,$16$,$18$,$20$},
        xlabel = {epoch}, ylabel = {Classification Accuracy}]
        \addplot table
        [x=epoch, y=val_accuracy, col sep=comma, mark = none] {Data/gd_10min.log};
        \addplot table
        [x=epoch, y=val_accuracy, col sep=comma] {Data/GD_05.log};
        \addplot table
        [x=epoch, y=val_accuracy, col sep=comma] {Data/GD_1.log};
        \addplot table
        [x=epoch, y=val_accuracy, col sep=comma]
        {Data/SGD_01_b32.log};
        
         \addlegendentry{GD$_{0.01}$}
        \addlegendentry{GD$_{0.05}$}
        \addlegendentry{GD$_{0.1}$}
        \addlegendentry{SGD$_{0.01}$}
      \end{axis}
    \end{tikzpicture}
    %\caption{Classification accuracy}
  \end{subfigure}
  \begin{subfigure}[b]{\textwidth}
    \begin{tikzpicture}
      \begin{axis}[tick style = {draw = none}, width = \textwidth,
        height = 0.7\textwidth,
        ytick = {0, 1, 2, 3, 4},
        yticklabels = {$0$, $1$, $\phantom{0.}2$, $3$, $4$},
        xtick = {1, 3, 5,7,9,11,13,15,17,19},
        xticklabels = {$2$, $4$, $6$, $8$,
          $10$,$12$,$14$,$16$,$18$,$20$},
        xlabel = {epoch}, ylabel = {Error Measure}]
        \addplot table
        [x=epoch, y=val_loss, col sep=comma] {Data/GD_01.log};
        \addplot table
        [x=epoch, y=val_loss, col sep=comma] {Data/GD_05.log};
        \addplot table
        [x=epoch, y=val_loss, col sep=comma] {Data/GD_1.log};
        \addplot table
        [x=epoch, y=val_loss, col sep=comma] {Data/SGD_01_b32.log};
        
        \addlegendentry{GD$_{0.01}$}
        \addlegendentry{GD$_{0.05}$}
        \addlegendentry{GD$_{0.1}$}
        \addlegendentry{SGD$_{0.01}$}
        
      \end{axis}
    \end{tikzpicture}
    \caption{Performance metrics during training}
  \end{subfigure}
  \\~\\
  \begin{subfigure}[b]{1.0\linewidth}
    \begin{tabu} to \textwidth {@{}  *4{X[c]}c*4{X[c]} @{}}
      \multicolumn{4}{c}{Classification Accuracy}
      &~&\multicolumn{4}{c}{Error Measure}
      \\\cline{1-4}\cline{6-9}
      GD$_{0.01}$&GD$_{0.05}$&GD$_{0.1}$&SGD$_{0.01}$&&GD$_{0.01}$&GD$_{0.05}$&GD$_{0.1}$&SGD$_{0.01}$
      \\\cline{1-4}\cline{6-9}
      1&1&1&1&&1&1&1&1
    \end{tabu}
    \caption{Performace metrics after 20 epochs}
  \end{subfigure}
  \caption{The neural network given in ?? trained with different
    algorithms on the MNIST handwritten digits data set. For gradient
    descent the learning rated 0.01, 0.05 and 0.1 are (GD$_{
        rate}$). For
    stochastic gradient descend a batch size of 32 and learning rate
    of 0.01 is used (SDG$_{0.01}$)}
\end{figure}

\begin{center}
  \begin{figure}[h]
    \centering
  \begin{subfigure}{0.19\textwidth}
    \includegraphics[width=\textwidth]{Data/mnist0.pdf}
  \end{subfigure}
  \begin{subfigure}{0.19\textwidth}
    \includegraphics[width=\textwidth]{Data/mnist1.pdf}
  \end{subfigure}
  \begin{subfigure}{0.19\textwidth}
    \includegraphics[width=\textwidth]{Data/mnist2.pdf}
  \end{subfigure}
  \begin{subfigure}{0.19\textwidth}
    \includegraphics[width=\textwidth]{Data/mnist3.pdf}
  \end{subfigure}
  \begin{subfigure}{0.19\textwidth}
    \includegraphics[width=\textwidth]{Data/mnist4.pdf}
  \end{subfigure}\\
  \begin{subfigure}{0.19\textwidth}
    \includegraphics[width=\textwidth]{Data/mnist5.pdf}
  \end{subfigure}
  \begin{subfigure}{0.19\textwidth}
    \includegraphics[width=\textwidth]{Data/mnist6.pdf}
  \end{subfigure}
  \begin{subfigure}{0.19\textwidth}
    \includegraphics[width=\textwidth]{Data/mnist7.pdf}
  \end{subfigure}
  \begin{subfigure}{0.19\textwidth}
    \includegraphics[width=\textwidth]{Data/mnist8.pdf}
  \end{subfigure}
  \begin{subfigure}{0.19\textwidth}
    \includegraphics[width=\textwidth]{Data/mnist9.pdf}
  \end{subfigure}
  \caption{The MNIST data set contains 70.000 images of preprocessed handwritten
    digits. Of these images 60.000 are used as training images, while
    the rest are used to validate the models trained.}
\end{figure}
\end{center}

\begin{figure}
  \begin{adjustbox}{width=\textwidth}
    \begin{tikzpicture}  
      \begin{scope}[x = (0:1cm), y=(90:1cm), z=(15:-0.5cm)]
        \node[canvas is xy plane at z=0, transform shape] at (0,0)
        {\includegraphics[width=5cm]{Data/klammern_r.jpg}};
        \node[canvas is xy plane at z=2, transform shape] at (0,-0.2)
        {\includegraphics[width=5cm]{Data/klammern_g.jpg}};
        \node[canvas is xy plane at z=4, transform shape] at (0,-0.4)
        {\includegraphics[width=5cm]{Data/klammern_b.jpg}};
        \node[canvas is xy plane at z=4, transform shape] at (-8,-0.2)
        {\includegraphics[width=5.3cm]{Data/klammern_rgb.jpg}};
      \end{scope}
    \end{tikzpicture}
  \end{adjustbox}
  \caption{On the right the red, green and blue chanels of the picture
    are displayed. In order to better visualize the color channes the
    black and white picture of each channel has been colored in the
    respective color. Combining the layers results in the image on the
    left}
\end{figure}


\end{document}

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