diff --git a/TeX/Plots/gen_dropout.tex b/TeX/Plots/gen_dropout.tex
index d29536d..016ea16 100644
--- a/TeX/Plots/gen_dropout.tex
+++ b/TeX/Plots/gen_dropout.tex
@@ -4,8 +4,8 @@ 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)
+(0.15cm,0cm)        %% default is (0.3cm,0cm)
+(0.3cm,0cm)         %% default is (0.6cm,0cm)
 };%
 }
 }
@@ -15,7 +15,8 @@ plot coordinates {
       \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]
+        xlabel = {epoch}, ylabel = {Classification 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]
         {Plots/Data/adam_datagen_full_mean.log};  
@@ -47,20 +48,20 @@ plot coordinates {
     \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&0.9914&0.9918&0.9928&0.9937&0.9938&0.9940 \Tstrut \\
-      max& \\
-      min& \\
-      \multicolumn{7}{c}{Training Accuracy}\Bstrut
-      \\\hline
-      mean&0.9994&0.9990&0.9989&0.9967&0.9954&0.9926 \Tstrut \\
-      max& \\
-      min& \\
-      
+    \begin{tabu} to \textwidth {@{}lc*5{X[c]}@{}}
+      \Tstrut \Bstrut & \textsc{\,Adam\,} & D. 0.2 & D. 0.4 & G. &G.+D.\,0.2 & G.+D.\,0.4 \\
+      \hline           
+      \multicolumn{7}{c}{Classification Accuracy}\Bstrut                       \\
+      \cline{2-7}
+      mean     \Tstrut   & 0.9914 & 0.9923 & 0.9930 & 0.9937 & 0.9938 & 0.9943 \\
+      max                & 0.9926 & 0.9930 & 0.9934 & 0.9946 & 0.9955 & 0.9956 \\
+      min                & 0.9887 & 0.9909 & 0.9922 & 0.9929 & 0.9929 & 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 and maximum accuracy after 48 epochs of training.}
   \end{subfigure}
diff --git a/TeX/Plots/pfg_test.tex b/TeX/Plots/pfg_test.tex
index d75e7fb..1ce8723 100644
--- a/TeX/Plots/pfg_test.tex
+++ b/TeX/Plots/pfg_test.tex
@@ -30,7 +30,8 @@ plot coordinates {
       \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]
+        xlabel = {epoch}, ylabel = {Classification Accuracy}, cycle
+        list/Dark2, every axis plot/.append style={line width =1.25pt}]
         % \addplot [dashed] table
         % [x=epoch, y=accuracy, col sep=comma, mark = none]
         % {Data/adam_datagen_full.log};
@@ -68,26 +69,26 @@ plot coordinates {
     \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& \\
-      
+    \begin{tabu} to \textwidth {@{}lc*5{X[c]}@{}}
+      \Tstrut \Bstrut & \textsc{\,Adam\,} & D. 0.2 & D. 0.4 & G. &G.+D.\,0.2 & G.+D.\,0.4 \\
+      \hline           
+      \multicolumn{7}{c}{Classification Accuracy}\Bstrut                       \\
+      \cline{2-7}
+      mean     \Tstrut   & 0.9914 & 0.9923 & 0.9930 & 0.9937 & 0.9938 & 0.9943 \\
+      max                & 0.9926 & 0.9930 & 0.9934 & 0.9946 & 0.9955 & 0.9956 \\
+      min                & 0.9887 & 0.9909 & 0.9922 & 0.9929 & 0.9929 & 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 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
+    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
diff --git a/TeX/further_applications_of_nn.tex b/TeX/further_applications_of_nn.tex
index 422df1b..e7b709b 100644
--- a/TeX/further_applications_of_nn.tex
+++ b/TeX/further_applications_of_nn.tex
@@ -695,6 +695,11 @@ mirroring.
     ... Additionally mirroring is not used for ...  reasons.}
 \end{figure}
 
+In order to compare the benefits obtained from implementing these
+measures we have trained the network given in ... on the same problem
+and implemented different combinations of the measures. The results
+are given in Figure~\ref{fig:gen_dropout}. Here it can be seen that ...
+
 \input{Plots/gen_dropout.tex}
 
 \todo{Vergleich verschiedene dropout größen auf MNSIT o.ä., subset als