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94 lines
3.3 KiB
TeX
94 lines
3.3 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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(0.0cm,0cm) %% default is (0.3cm,0cm)
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(0.0cm,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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\begin{axis}[tick style = {draw = none}, width = \textwidth,
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height = 0.6\textwidth,
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xtick = {1, 3, 5,7,9,11,13,15,17,19},
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xticklabels = {$2$, $4$, $6$, $8$,
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$10$,$12$,$14$,$16$,$18$,$20$},
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xlabel = {training epoch}, ylabel = {classification accuracy}]
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\addplot table
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[x=epoch, y=val_accuracy, col sep=comma] {Plots/Data/GD_01.log};
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\addplot table
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[x=epoch, y=val_accuracy, col sep=comma] {Plots/Data/GD_05.log};
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\addplot table
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[x=epoch, y=val_accuracy, col sep=comma] {Plots/Data/GD_1.log};
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\addplot table
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[x=epoch, y=val_accuracy, col sep=comma]
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{Plots/Data/SGD_01_b32.log};
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\addlegendentry{GD$_{0.01}$}
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\addlegendentry{GD$_{0.05}$}
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\addlegendentry{GD$_{0.1}$}
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\addlegendentry{SGD$_{0.01}$}
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\end{axis}
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\end{tikzpicture}
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%\caption{Classification accuracy}
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\end{subfigure}
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\begin{subfigure}[b]{\textwidth}
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\begin{tikzpicture}
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\begin{axis}[tick style = {draw = none}, width = \textwidth,
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height = 0.6\textwidth,
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ytick = {0, 1, 2, 3, 4},
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yticklabels = {$0$, $1$, $\phantom{0.}2$, $3$, $4$},
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xtick = {1, 3, 5,7,9,11,13,15,17,19},
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xticklabels = {$2$, $4$, $6$, $8$,
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$10$,$12$,$14$,$16$,$18$,$20$},
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xlabel = {training epoch}, ylabel = {error measure\vphantom{fy}}]
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\addplot table
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[x=epoch, y=val_loss, col sep=comma] {Plots/Data/GD_01.log};
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\addplot table
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[x=epoch, y=val_loss, col sep=comma] {Plots/Data/GD_05.log};
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\addplot table
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[x=epoch, y=val_loss, col sep=comma] {Plots/Data/GD_1.log};
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\addplot table
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[x=epoch, y=val_loss, col sep=comma] {Plots/Data/SGD_01_b32.log};
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\addlegendentry{GD$_{0.01}$}
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\addlegendentry{GD$_{0.05}$}
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\addlegendentry{GD$_{0.1}$}
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\addlegendentry{SGD$_{0.01}$}
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\end{axis}
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\end{tikzpicture}
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\caption{Performance metrics during training}
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\end{subfigure}
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% \\~\\
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\caption{The neural network given in ?? trained with different
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algorithms on the MNIST handwritten digits data set. For gradient
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descent the learning rated 0.01, 0.05 and 0.1 are (GD$_{\cdot}$). For
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stochastic gradient descend a batch size of 32 and learning rate
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of 0.01 is used (SDG$_{0.01}$).}
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\label{fig:sgd_vs_gd}
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\end{figure}
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\begin{table}[h]
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\begin{tabu} to \textwidth {@{} *4{X[c]}c*4{X[c]} @{}}
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\multicolumn{4}{c}{Classification Accuracy}
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&~&\multicolumn{4}{c}{Error Measure}
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\\\cline{1-4}\cline{6-9}
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GD$_{0.01}$&GD$_{0.05}$&GD$_{0.1}$&SGD$_{0.01}$&&GD$_{0.01}$&GD$_{0.05}$&GD$_{0.1}$&SGD$_{0.01}$
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\\\cline{1-4}\cline{6-9}
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\multicolumn{9}{c}{test}\\
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0.265&0.633&0.203&0.989&&2.267&1.947&3.91&0.032
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\end{tabu}
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\caption{Performance metrics of the networks trained in
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Figure~\ref{fig:sgd_vs_gd} after 20 training epochs.}
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\label{table:sgd_vs_gd}
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\end{table}
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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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