training + validation results for vgg

anphetamina · Feb 12, 2020 · 0bb74c6 · 0bb74c6
1 parent 12523cb
commit 0bb74c6
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diff --git a/models/vgg11.ipynb b/models/vgg11.ipynb
diff --git a/models/vgg19.ipynb b/models/vgg19.ipynb
diff --git a/report/egpaper_final.aux b/report/egpaper_final.aux
@@ -38,8 +38,8 @@
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-\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Best values per hyperparameter.}}{3}{table.1}\protected@file@percent }
-\newlabel{tab:sets}{{1}{3}{Best values per hyperparameter}{table.1}{}}
+\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Best values per hyperparameter and network.}}{3}{table.1}\protected@file@percent }
+\newlabel{tab:sets}{{1}{3}{Best values per hyperparameter and network}{table.1}{}}
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 \@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces Average validation accuracy per network and split in 100 epochs.}}{3}{table.2}\protected@file@percent }

diff --git a/report/egpaper_final.fdb_latexmk b/report/egpaper_final.fdb_latexmk
@@ -1,12 +1,12 @@
 # Fdb version 3
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+["bibtex egpaper_final"] 1581529649 "egpaper_final.aux" "egpaper_final.bbl" "egpaper_final" 1581533705
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-  "egpaper_final.aux" 1581523814 4356 1d66625c4ad2c0a15cc5cfa840d866e3 "pdflatex"
+  "egpaper_final.aux" 1581533705 4380 1f8bfcc04b708676b2187f311556ef32 "pdflatex"
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+  "egpaper_final.blg"
+["pdflatex"] 1581533704 "d:/asant/Desktop/AIML_project/report/egpaper_final.tex" "d:/asant/Desktop/AIML_project/report/egpaper_final.pdf" "egpaper_final" 1581533705
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@@ -93,19 +93,19 @@
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-  "d:/asant/Desktop/AIML_project/report/egpaper_final.tex" 1581523807 16531 771253f09356ac85b1b5389714d8cf38 ""
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-  "egpaper_final.tex" 1581523807 16531 771253f09356ac85b1b5389714d8cf38 ""
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+  "egpaper_final.tex" 1581533701 16552 86bdcf797a3154d6dede7978a6b5913b ""
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   "img/voting-system.png" 1581366471 1822764 006064d747a755f704c7aaf5b173d5de ""
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-  "d:/asant/Desktop/AIML_project/report/egpaper_final.log"
   "egpaper_final.log"
   "egpaper_final.pdf"
-  "egpaper_final.aux"
   "d:/asant/Desktop/AIML_project/report/egpaper_final.pdf"
+  "egpaper_final.aux"
+  "d:/asant/Desktop/AIML_project/report/egpaper_final.log"
diff --git a/report/egpaper_final.pdf b/report/egpaper_final.pdf
diff --git a/report/egpaper_final.synctex.gz b/report/egpaper_final.synctex.gz
diff --git a/report/egpaper_final.tex b/report/egpaper_final.tex
@@ -123,7 +123,7 @@ \section{Training phase}\label{training}
 
 The research method to find the optimal set of hyperparameters has been the same for all networks\footnote {ResNet50 \& ResNet152, VGG11 \& VGG19 and GoogLeNet (Inception v1)} except for a slight difference related to GoogLeNet due to its three output branches. A lot of experiments have been done on different network variants of the same model to evaluate the impact of the networks' depth on the results.
 
-The first step was to find a good starting hyperparameters set to make the network accomplish a full training. Due to Google Colab limitations, a random search has been used to evaluate 50 different hyperparameters sets, the approximate best ones have been reported in Table \ref{tab:sets}.
+The first step was to find a good starting hyperparameters set to make the network accomplish a full training. Due to Google Colab limitations, a random search has been used to evaluate 50 different hyperparameters sets, the best ones have been reported in Table \ref{tab:sets}.
 
 \begin{table}
    \begin{center}
@@ -135,14 +135,14 @@ \section{Training phase}\label{training}
    \hline
    ResNet50 & 0.0007 & 14 & 8e-04 & 0.7 \\
    \hline
-   VGG19 & 0.003 & 8 & 3e-04 & 0.01 \\
+   VGG19 & 0.002 & 11 & 1e-06 & 0.1 \\
    \hline
-   VGG11 & 0.0005 & 8 & 3e-05 & 0.05 \\
+   VGG11 & 0.0009 & 11 & 7e-04 & 0.07 \\
    \hline
    GoogLeNet & 0.0001 & 8 & 5e-05 & 0.1 \\
    \end{tabular}
    \end{center}
-   \caption{Best values per hyperparameter.}
+   \caption{Best values per hyperparameter and network.}
    \label{tab:sets}
    \end{table}
 
@@ -162,9 +162,9 @@ \section{Training phase}\label{training}
    \hline
    ResNet50 & \% & 62\% & \% \\
    \hline
-   VGG19 & \% & 61\% & \% \\
+   VGG19 & 72\% & 67\% & 80\% \\
    \hline
-   VGG11 & \% & 65\% & \% \\
+   VGG11 & 74\% & 77\% & 84\% \\
    \hline
    GoogLeNet & \% & \% & 82\% \\
    \end{tabular}
@@ -194,11 +194,11 @@ \section{Testing}\label{testing}
       \hline
       ResNet50 & \% & \% & \% \\
       \hline
-      VGG19 & \% & \% & \% \\
+      VGG19 & 17\% & 16\% & 15\% \\
       \hline
-      VGG11 & \% & \% & \% \\
+      VGG11 & 31\% & 13\% & 33\% \\
       \hline
-      GoogLeNet & 37\% & \% & \% \\
+      GoogLeNet & \% & \% & 37\% \\
       \end{tabular}
       \end{center}
       \caption{Test accuracy per network and split.}