Masterarbeit/TeX/Plots/pfg_test.tex

\documentclass[a4paper, 12pt, draft=true]{article}
\usepackage{pgfplots}
\usepackage{filecontents}
\usepackage{subcaption}
\usepackage{adjustbox}
\usepackage{xcolor}
\usepackage{tabu}
\usepackage{graphicx}
\usetikzlibrary{calc, 3d}
\usepgfplotslibrary{colorbrewer}

\newcommand\Tstrut{\rule{0pt}{2.6ex}}         % = `top' strut
\newcommand\Bstrut{\rule[-0.9ex]{0pt}{0pt}}   % = `bottom' strut

\begin{document}
\pgfplotsset{
compat=1.11,
legend image code/.code={
\draw[mark repeat=2,mark phase=2]
plot coordinates {
(0cm,0cm)
(0.3cm,0cm)        %% default is (0.3cm,0cm)
(0.6cm,0cm)         %% default is (0.6cm,0cm)
};%
}
}
\begin{figure}
  \begin{subfigure}[h]{\textwidth}
    \begin{tikzpicture}
      \begin{axis}[legend cell align={left},yticklabel style={/pgf/number format/fixed,
                     /pgf/number format/precision=3},tick style = {draw = none}, width = \textwidth,
        height = 0.6\textwidth, ymin = 0.988, legend style={at={(0.9825,0.0175)},anchor=south east},
        xlabel = {epoch}, ylabel = {Classification Accuracy}, cycle list/Dark2]
        % \addplot [dashed] table
        % [x=epoch, y=accuracy, col sep=comma, mark = none]
        % {Data/adam_datagen_full.log};
        \addplot table
        [x=epoch, y=val_accuracy, col sep=comma, mark = none]
        {Data/adam_datagen_full_mean.log};  
        % \addplot [dashed] table
        % [x=epoch, y=accuracy, col sep=comma, mark = none]
        % {Data/adam_datagen_dropout_02_full.log}; 
        \addplot table
        [x=epoch, y=val_accuracy, col sep=comma, mark = none]
        {Data/adam_datagen_dropout_02_full_mean.log}; 
        \addplot table
        [x=epoch, y=val_accuracy, col sep=comma, mark = none]
        {Data/adam_datagen_dropout_04_full_mean.log};
        \addplot table
        [x=epoch, y=val_accuracy, col sep=comma, mark = none]
        {Data/adam_dropout_02_full_mean.log}; 
        \addplot table
        [x=epoch, y=val_accuracy, col sep=comma, mark = none]
        {Data/adam_dropout_04_full_mean.log}; 
        \addplot [dashed] table
        [x=epoch, y=val_accuracy, col sep=comma, mark = none]
        {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{Classification accuracy}
    \vspace{.25cm}
  \end{subfigure}
  \begin{subfigure}[h]{1.0\linewidth}
    \begin{tabu} to \textwidth {@{} l *6{X[c]} @{}}
      \multicolumn{7}{c}{Classification Accuracy}\Bstrut
      \\\hline
      &\textsc{Adam}&D. 0.2&D. 0.4&G.&G.+D.~0.2&G.~,D.~0.4 \Tstrut \Bstrut
      \\\hline
      mean&0.9994&0.9990&0.9989&0.9937&0.9938&0.9940 \Tstrut \\
      max& \\
      min& \\
      \multicolumn{7}{c}{Training Accuracy}\Bstrut
      \\\hline
      mean&0.9914&0.9918&0.9928&0.9937&0.9938&0.9940 \Tstrut \\
      max& \\
      min& \\
      
    \end{tabu}
    \caption{Mean and maximum accuracy after 48 epochs of training.}
  \end{subfigure}
  \caption{Accuracy for the net given in ... 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 5 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).}
\end{figure}

\begin{center}
  \begin{figure}[h]
    \centering
  \begin{subfigure}{0.19\textwidth}
    \includegraphics[width=\textwidth]{Data/mnist0.pdf}
    \caption{original\\image}
  \end{subfigure}
  \begin{subfigure}{0.19\textwidth}
    \includegraphics[width=\textwidth]{Data/mnist_gen_zoom.pdf}
    \caption{random\\zoom}
  \end{subfigure}
  \begin{subfigure}{0.19\textwidth}
    \includegraphics[width=\textwidth]{Data/mnist_gen_shear.pdf}
    \caption{random\\shear}
  \end{subfigure}
  \begin{subfigure}{0.19\textwidth}
    \includegraphics[width=\textwidth]{Data/mnist_gen_rotation.pdf}
    \caption{random\\rotation}
  \end{subfigure}
  \begin{subfigure}{0.19\textwidth}
    \includegraphics[width=\textwidth]{Data/mnist_gen_shift.pdf}
  \caption{random\\positional shift}
  \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}

\begin{figure}
  \centering
  \begin{subfigure}{.45\linewidth}
    \centering
    \begin{tikzpicture}
      \begin{axis}[enlargelimits=false, ymin=0, ymax = 1, width=\textwidth]
        \addplot [domain=-5:5, samples=101,unbounded coords=jump]{1/(1+exp(-x)};
      \end{axis}
    \end{tikzpicture}
  \end{subfigure}
  \begin{subfigure}{.45\linewidth}
    \centering
    \begin{tikzpicture}
      \begin{axis}[enlargelimits=false, width=\textwidth]
        \addplot[domain=-5:5, samples=100]{tanh(x)};
      \end{axis}
    \end{tikzpicture}
  \end{subfigure}
  \begin{subfigure}{.45\linewidth}
    \centering
    \begin{tikzpicture}
      \begin{axis}[enlargelimits=false, width=\textwidth,
        ytick={0,2,4},yticklabels={\hphantom{4.}0,2,4}, ymin=-1]
        \addplot[domain=-5:5, samples=100]{max(0,x)};
      \end{axis}
    \end{tikzpicture}
  \end{subfigure}
  \begin{subfigure}{.45\linewidth}
    \centering
    \begin{tikzpicture}
      \begin{axis}[enlargelimits=false, width=\textwidth, ymin=-1,
        ytick={0,2,4},yticklabels={$\hphantom{-5.}0$,2,4}]
        \addplot[domain=-5:5, samples=100]{max(0,x)+ 0.1*min(0,x)};
      \end{axis}
    \end{tikzpicture}
  \end{subfigure}
\end{figure}


\begin{tikzpicture}
\begin{axis}[enlargelimits=false]
\addplot [domain=-5:5, samples=101,unbounded coords=jump]{1/(1+exp(-x)};
\addplot[domain=-5:5, samples=100]{tanh(x)};
\addplot[domain=-5:5, samples=100]{max(0,x)};
\end{axis}
\end{tikzpicture}

\begin{tikzpicture}
\begin{axis}[enlargelimits=false]
\addplot[domain=-2*pi:2*pi, samples=100]{cos(deg(x))};
\end{axis}
\end{tikzpicture}

\end{document}

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