diff --git a/LaTeX/Final_Project_Loedige.tex b/LaTeX/Final_Project_Loedige.tex index ac17787..117edfe 100644 --- a/LaTeX/Final_Project_Loedige.tex +++ b/LaTeX/Final_Project_Loedige.tex @@ -1,4 +1,10 @@ \documentclass[a4paper, 12pt, english]{article} +\usepackage[ + top=2cm, + bottom=2cm, + right=2cm, + left=2cm +]{geometry} \usepackage[utf8]{inputenc} \usepackage[T1]{fontenc} @@ -8,6 +14,7 @@ \usepackage{multicol} \usepackage{setspace} \usepackage{graphicx} +\usepackage{xurl} % allow deeeep lists \usepackage{enumitem} @@ -46,6 +53,7 @@ \begin{document} \maketitle \toccontents +\clearpage \section{Assignment}% \label{sec:Assignment} @@ -104,103 +112,67 @@ \item Put all the above in a single PDF and upload to ITC-LMS \end{itemize} \end{itemize} - -\subsection{How the project is graded}% -\label{sub:How the project is graded} -\begin{itemize} - \item The purpose of the project is to make you familiar with machine learning experiments. - \begin{itemize} - \item Not intended to be a stressful project. - \begin{itemize} - \item So do not worry too much on achieving high accuracy, good parameter settings, etc - \item Make sure to keep it simple - \end{itemize} - \end{itemize} - \item What we would like to see: - \begin{itemize} - \item Clarity of the project description - \item Relevance between the problem and the chosen approaches - \item Appropriate input/output design and execution of the experiments - \item Proper evaluation methods, figures and tables - \item Easy to understand writings, informative comments on the code - \end{itemize} - \item Overall grade for this course : 70\% homeworks, 30\% final project - \begin{itemize} - \item Bonus points for helping each other on slack - \begin{itemize} - \item Feel free to discuss your final project on slack if you have troubles - \end{itemize} - \end{itemize} -\end{itemize} \clearpage -%\section{Code}% -%\label{sec:Code} +\section{Project Goal}% +\label{sec:Project Goal} +This project aims to compare different deep learning techniques. +The goal is to discern the pros and cons of the different techniques when it comes to their use on character recognitions in images. +This approach was chosen because it doesn't focus on the learning of object concepts but rather on pattern recognition. +Since pattern recognition and character recognition in particular is a less complex topic than concept classification +it allows for the use of less complex models which are easier to understand. -%\definecolor{mGreen}{rgb}{0,0.6,0} -%\definecolor{mGray}{rgb}{0.5,0.5,0.5} -%\definecolor{mPurple}{rgb}{0.58,0,0.82} -%\definecolor{backgroundColour}{rgb}{0.95,0.95,0.92} -%\lstset{ - %language=C, - %backgroundcolor=\color{backgroundColour}, - %commentstyle=\color{mGreen}, - %keywordstyle=\color{magenta}, - %numberstyle=\tiny\color{mGray}, - %stringstyle=\color{mPurple}, - %basicstyle=\ttfamily\scriptsize, - %breakatwhitespace=false, - %breaklines=true, - %captionpos=b, - %keepspaces=true, - %numbers=left, - %firstnumber=0, - %stepnumber=1, - %numbersep=5pt, - %showspaces=false, - %showstringspaces=false, - %showtabs=false, - %tabsize=2, - %literate={~}{{$\mathtt{\sim}$}}1 -%} -%\lstset{literate=% - %{Ö}{{\"O}}1 - %{Ä}{{\"A}}1 - %{Ü}{{\"U}}1 - %{ß}{{\ss}}2 - %{ü}{{\"u}}1 - %{ä}{{\"a}}1 - %{ö}{{\"o}}1 -%} +\subsection{The Dataset}% +\label{sub:The Dataset} +The project uses the "svhn\_cropped" dataset (\url{https://www.tensorflow.org/datasets/catalog/svhn_cropped}). +The Street View House Numbers (SVHN) dataset was created at Stanford University and includes +"73\,257 digits for training, 26\,032 digits for testing, +and 531\,131 additional, somewhat less difficult samples, to use as extra training data" (\url{http://ufldl.stanford.edu/housenumbers/}). +The dataset provided by TensorFlow uses the "MNIST-like 32-by-32 images centered around a single character" version. -%\subsection{\texttt{vinput.c}}% -%\label{sub:vinput_c} -%\lstinputlisting{../Code/vinput.c} -%\clearpage +\clearpage -%\subsection{\texttt{vinput.h}}% -%\label{sub:vinput_h} -%\lstinputlisting{../Code/vinput.h} -%\clearpage +\section{Code}% +\label{sec:Code} -%\subsection{\texttt{vkbd.c}}% -%\label{sub:vkbd} -%\lstinputlisting{../Code/vkbd.c} -%\clearpage +The following code is an exported and slightly formatted version of a Jupyter Notebook that is also available at \url{https://git.ploedige.com/Intelligent_World_Informatics_V/Final_Project}. -%\section{Output}% -%\label{sec:Output} +\definecolor{mGreen}{rgb}{0,0.6,0} +\definecolor{mGray}{rgb}{0.5,0.5,0.5} +\definecolor{mPurple}{rgb}{0.58,0,0.82} +\definecolor{backgroundColour}{rgb}{0.95,0.95,0.92} +\lstset{ + language=python, + backgroundcolor=\color{backgroundColour}, + commentstyle=\color{mGreen}, + keywordstyle=\color{magenta}, + numberstyle=\tiny\color{mGray}, + stringstyle=\color{mPurple}, + basicstyle=\ttfamily\scriptsize, + breakatwhitespace=false, + breaklines=true, + captionpos=b, + keepspaces=true, + numbers=left, + firstnumber=0, + stepnumber=1, + numbersep=5pt, + showspaces=false, + showstringspaces=false, + showtabs=false, + tabsize=2, + literate={~}{{$\mathtt{\sim}$}}1 +} +\lstset{literate=% + {Ö}{{\"O}}1 + {Ä}{{\"A}}1 + {Ü}{{\"U}}1 + {ß}{{\ss}}2 + {ü}{{\"u}}1 + {ä}{{\"a}}1 + {ö}{{\"o}}1 +} -%\subsection{Shell Script for compiling and running}% -%\label{sub:Shell Script for compiling and running} -%\includegraphics[width=\textwidth]{run_sh.png} - -%\subsection{Terminal Screenshot}% -%\label{sub:Terminal Screenshot} -%\includegraphics[width=\textwidth]{executing_screenshot.png} - -%\subsection{\texttt{output.txt}}% -%\label{sub:output_txt} -%\includegraphics[width=\textwidth]{output_txt.png} +\lstinputlisting{./exported_code.py} \end{document}