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