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notebook.tex
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% Default to the notebook output style
% Inherit from the specified cell style.
\documentclass[11pt]{article}
\usepackage[T1]{fontenc}
% Nicer default font (+ math font) than Computer Modern for most use cases
\usepackage{mathpazo}
% Basic figure setup, for now with no caption control since it's done
% automatically by Pandoc (which extracts  syntax from Markdown).
\usepackage{graphicx}
% We will generate all images so they have a width \maxwidth. This means
% that they will get their normal width if they fit onto the page, but
% are scaled down if they would overflow the margins.
\makeatletter
\def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth
\else\Gin@nat@width\fi}
\makeatother
\let\Oldincludegraphics\includegraphics
% Set max figure width to be 80% of text width, for now hardcoded.
\renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=.8\maxwidth]{#1}}
% Ensure that by default, figures have no caption (until we provide a
% proper Figure object with a Caption API and a way to capture that
% in the conversion process - todo).
\usepackage{caption}
\DeclareCaptionLabelFormat{nolabel}{}
\captionsetup{labelformat=nolabel}
\usepackage{adjustbox} % Used to constrain images to a maximum size
\usepackage{xcolor} % Allow colors to be defined
\usepackage{enumerate} % Needed for markdown enumerations to work
\usepackage{geometry} % Used to adjust the document margins
\usepackage{amsmath} % Equations
\usepackage{amssymb} % Equations
\usepackage{textcomp} % defines textquotesingle
% Hack from http://tex.stackexchange.com/a/47451/13684:
\AtBeginDocument{%
\def\PYZsq{\textquotesingle}% Upright quotes in Pygmentized code
}
\usepackage{upquote} % Upright quotes for verbatim code
\usepackage{eurosym} % defines \euro
\usepackage[mathletters]{ucs} % Extended unicode (utf-8) support
\usepackage[utf8x]{inputenc} % Allow utf-8 characters in the tex document
\usepackage{fancyvrb} % verbatim replacement that allows latex
\usepackage{grffile} % extends the file name processing of package graphics
% to support a larger range
% The hyperref package gives us a pdf with properly built
% internal navigation ('pdf bookmarks' for the table of contents,
% internal cross-reference links, web links for URLs, etc.)
\usepackage{hyperref}
\usepackage{longtable} % longtable support required by pandoc >1.10
\usepackage{booktabs} % table support for pandoc > 1.12.2
\usepackage[inline]{enumitem} % IRkernel/repr support (it uses the enumerate* environment)
\usepackage[normalem]{ulem} % ulem is needed to support strikethroughs (\sout)
% normalem makes italics be italics, not underlines
% Colors for the hyperref package
\definecolor{urlcolor}{rgb}{0,.145,.698}
\definecolor{linkcolor}{rgb}{.71,0.21,0.01}
\definecolor{citecolor}{rgb}{.12,.54,.11}
% ANSI colors
\definecolor{ansi-black}{HTML}{3E424D}
\definecolor{ansi-black-intense}{HTML}{282C36}
\definecolor{ansi-red}{HTML}{E75C58}
\definecolor{ansi-red-intense}{HTML}{B22B31}
\definecolor{ansi-green}{HTML}{00A250}
\definecolor{ansi-green-intense}{HTML}{007427}
\definecolor{ansi-yellow}{HTML}{DDB62B}
\definecolor{ansi-yellow-intense}{HTML}{B27D12}
\definecolor{ansi-blue}{HTML}{208FFB}
\definecolor{ansi-blue-intense}{HTML}{0065CA}
\definecolor{ansi-magenta}{HTML}{D160C4}
\definecolor{ansi-magenta-intense}{HTML}{A03196}
\definecolor{ansi-cyan}{HTML}{60C6C8}
\definecolor{ansi-cyan-intense}{HTML}{258F8F}
\definecolor{ansi-white}{HTML}{C5C1B4}
\definecolor{ansi-white-intense}{HTML}{A1A6B2}
% commands and environments needed by pandoc snippets
% extracted from the output of `pandoc -s`
\providecommand{\tightlist}{%
\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}
\DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
% Add ',fontsize=\small' for more characters per line
\newenvironment{Shaded}{}{}
\newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
\newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}}
\newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
\newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\StringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}}
\newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}}
\newcommand{\AlertTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
\newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.02,0.16,0.49}{{#1}}}
\newcommand{\RegionMarkerTok}[1]{{#1}}
\newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
\newcommand{\NormalTok}[1]{{#1}}
% Additional commands for more recent versions of Pandoc
\newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.53,0.00,0.00}{{#1}}}
\newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
\newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.73,0.40,0.53}{{#1}}}
\newcommand{\ImportTok}[1]{{#1}}
\newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.73,0.13,0.13}{\textit{{#1}}}}
\newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\CommentVarTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\VariableTok}[1]{\textcolor[rgb]{0.10,0.09,0.49}{{#1}}}
\newcommand{\ControlFlowTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
\newcommand{\OperatorTok}[1]{\textcolor[rgb]{0.40,0.40,0.40}{{#1}}}
\newcommand{\BuiltInTok}[1]{{#1}}
\newcommand{\ExtensionTok}[1]{{#1}}
\newcommand{\PreprocessorTok}[1]{\textcolor[rgb]{0.74,0.48,0.00}{{#1}}}
\newcommand{\AttributeTok}[1]{\textcolor[rgb]{0.49,0.56,0.16}{{#1}}}
\newcommand{\InformationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
\newcommand{\WarningTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}}
% Define a nice break command that doesn't care if a line doesn't already
% exist.
\def\br{\hspace*{\fill} \\* }
% Math Jax compatability definitions
\def\gt{>}
\def\lt{<}
% Document parameters
\title{Traffic\_Sign\_Classifier}
% Pygments definitions
\makeatletter
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\let\PY@ul=\relax \let\PY@tc=\relax%
\let\PY@bc=\relax \let\PY@ff=\relax}
\def\PY@tok#1{\csname PY@tok@#1\endcsname}
\def\PY@toks#1+{\ifx\relax#1\empty\else%
\PY@tok{#1}\expandafter\PY@toks\fi}
\def\PY@do#1{\PY@bc{\PY@tc{\PY@ul{%
\PY@it{\PY@bf{\PY@ff{#1}}}}}}}
\def\PY#1#2{\PY@reset\PY@toks#1+\relax+\PY@do{#2}}
\expandafter\def\csname PY@tok@fm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}}
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\expandafter\def\csname PY@tok@nv\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
\expandafter\def\csname PY@tok@sb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@sr\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.53}{##1}}}
\expandafter\def\csname PY@tok@cpf\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@gd\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.63,0.00,0.00}{##1}}}
\expandafter\def\csname PY@tok@vg\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
\expandafter\def\csname PY@tok@s1\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@nc\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}}
\expandafter\def\csname PY@tok@nn\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}}
\expandafter\def\csname PY@tok@sx\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
\expandafter\def\csname PY@tok@c\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@vi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
\expandafter\def\csname PY@tok@cm\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}}
\expandafter\def\csname PY@tok@gt\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.27,0.87}{##1}}}
\expandafter\def\csname PY@tok@na\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.49,0.56,0.16}{##1}}}
\expandafter\def\csname PY@tok@dl\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}}
\expandafter\def\csname PY@tok@kr\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}}
\expandafter\def\csname PY@tok@ow\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.67,0.13,1.00}{##1}}}
\expandafter\def\csname PY@tok@vm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
\expandafter\def\csname PY@tok@vc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}}
\def\PYZbs{\char`\\}
\def\PYZus{\char`\_}
\def\PYZob{\char`\{}
\def\PYZcb{\char`\}}
\def\PYZca{\char`\^}
\def\PYZam{\char`\&}
\def\PYZlt{\char`\<}
\def\PYZgt{\char`\>}
\def\PYZsh{\char`\#}
\def\PYZpc{\char`\%}
\def\PYZdl{\char`\$}
\def\PYZhy{\char`\-}
\def\PYZsq{\char`\'}
\def\PYZdq{\char`\"}
\def\PYZti{\char`\~}
% for compatibility with earlier versions
\def\PYZat{@}
\def\PYZlb{[}
\def\PYZrb{]}
\makeatother
% Exact colors from NB
\definecolor{incolor}{rgb}{0.0, 0.0, 0.5}
\definecolor{outcolor}{rgb}{0.545, 0.0, 0.0}
% Prevent overflowing lines due to hard-to-break entities
\sloppy
% Setup hyperref package
\hypersetup{
breaklinks=true, % so long urls are correctly broken across lines
colorlinks=true,
urlcolor=urlcolor,
linkcolor=linkcolor,
citecolor=citecolor,
}
% Slightly bigger margins than the latex defaults
\geometry{verbose,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in}
\begin{document}
\maketitle
\hypertarget{self-driving-car-engineer-nanodegree}{%
\section{Self-Driving Car Engineer
Nanodegree}\label{self-driving-car-engineer-nanodegree}}
\hypertarget{deep-learning}{%
\subsection{Deep Learning}\label{deep-learning}}
\hypertarget{project-build-a-traffic-sign-recognition-classifier}{%
\subsection{Project: Build a Traffic Sign Recognition
Classifier}\label{project-build-a-traffic-sign-recognition-classifier}}
In this notebook, a template is provided for you to implement your
functionality in stages, which is required to successfully complete this
project. If additional code is required that cannot be included in the
notebook, be sure that the Python code is successfully imported and
included in your submission if necessary.
\begin{quote}
\textbf{Note}: Once you have completed all of the code implementations,
you need to finalize your work by exporting the iPython Notebook as an
HTML document. Before exporting the notebook to html, all of the code
cells need to have been run so that reviewers can see the final
implementation and output. You can then export the notebook by using the
menu above and navigating to \n``,''\textbf{File -\textgreater{}
Download as -\textgreater{} HTML (.html)}. Include the finished document
along with this notebook as your submission.
\end{quote}
In addition to implementing code, there is a writeup to complete. The
writeup should be completed in a separate file, which can be either a
markdown file or a pdf document. There is a
\href{https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md}{write
up template} that can be used to guide the writing process. Completing
the code template and writeup template will cover all of the
\href{https://review.udacity.com/\#!/rubrics/481/view}{rubric points}
for this project.
The \href{https://review.udacity.com/\#!/rubrics/481/view}{rubric}
contains ``Stand Out Suggestions'' for enhancing the project beyond the
minimum requirements. The stand out suggestions are optional. If you
decide to pursue the ``stand out suggestions'', you can include the code
in this Ipython notebook and also discuss the results in the writeup
file.
\begin{quote}
\textbf{Note:} Code and Markdown cells can be executed using the
\textbf{Shift + Enter} keyboard shortcut. In addition, Markdown cells
can be edited by typically double-clicking the cell to enter edit mode.
\end{quote}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}224}]:} \PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
\PY{c+c1}{\PYZsh{} PREVENT MULTIPLE PRE PROCESSING}
\PY{n}{PRE\PYZus{}PROCESSING\PYZus{}DONE} \PY{o}{=} \PY{k+kc}{False}
\PY{n}{NUM\PYZus{}CLASSES} \PY{o}{=} \PY{l+m+mi}{43}
\end{Verbatim}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\hypertarget{step-0-load-the-data}{%
\subsection{Step 0: Load The Data}\label{step-0-load-the-data}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}225}]:} \PY{c+c1}{\PYZsh{} Load pickled data}
\PY{k+kn}{import} \PY{n+nn}{pickle}
\PY{c+c1}{\PYZsh{} TODO: Fill this in based on where you saved the training and testing data}
\PY{n}{training\PYZus{}file} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{./traffic\PYZhy{}signs\PYZhy{}data/train.p}\PY{l+s+s1}{\PYZsq{}}
\PY{n}{validation\PYZus{}file}\PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{./traffic\PYZhy{}signs\PYZhy{}data/valid.p}\PY{l+s+s1}{\PYZsq{}}
\PY{n}{testing\PYZus{}file} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{./traffic\PYZhy{}signs\PYZhy{}data/test.p}\PY{l+s+s1}{\PYZsq{}}
\PY{k}{with} \PY{n+nb}{open}\PY{p}{(}\PY{n}{training\PYZus{}file}\PY{p}{,} \PY{n}{mode}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{rb}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)} \PY{k}{as} \PY{n}{f}\PY{p}{:}
\PY{n}{train} \PY{o}{=} \PY{n}{pickle}\PY{o}{.}\PY{n}{load}\PY{p}{(}\PY{n}{f}\PY{p}{)}
\PY{k}{with} \PY{n+nb}{open}\PY{p}{(}\PY{n}{validation\PYZus{}file}\PY{p}{,} \PY{n}{mode}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{rb}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)} \PY{k}{as} \PY{n}{f}\PY{p}{:}
\PY{n}{valid} \PY{o}{=} \PY{n}{pickle}\PY{o}{.}\PY{n}{load}\PY{p}{(}\PY{n}{f}\PY{p}{)}
\PY{k}{with} \PY{n+nb}{open}\PY{p}{(}\PY{n}{testing\PYZus{}file}\PY{p}{,} \PY{n}{mode}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{rb}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)} \PY{k}{as} \PY{n}{f}\PY{p}{:}
\PY{n}{test} \PY{o}{=} \PY{n}{pickle}\PY{o}{.}\PY{n}{load}\PY{p}{(}\PY{n}{f}\PY{p}{)}
\PY{n}{X\PYZus{}train}\PY{p}{,} \PY{n}{y\PYZus{}train} \PY{o}{=} \PY{n}{train}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{features}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,} \PY{n}{train}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{labels}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
\PY{n}{X\PYZus{}valid}\PY{p}{,} \PY{n}{y\PYZus{}valid} \PY{o}{=} \PY{n}{valid}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{features}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,} \PY{n}{valid}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{labels}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
\PY{n}{X\PYZus{}test}\PY{p}{,} \PY{n}{y\PYZus{}test} \PY{o}{=} \PY{n}{test}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{features}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,} \PY{n}{test}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{labels}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}
\PY{c+c1}{\PYZsh{} print(train);}
\PY{c+c1}{\PYZsh{} print(X\PYZus{}train);}
\PY{c+c1}{\PYZsh{} print(y\PYZus{}train);}
\end{Verbatim}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\hypertarget{step-1-dataset-summary-exploration}{%
\subsection{Step 1: Dataset Summary \&
Exploration}\label{step-1-dataset-summary-exploration}}
The pickled data is a dictionary with 4 key/value pairs:
\begin{itemize}
\tightlist
\item
\texttt{\textquotesingle{}features\textquotesingle{}} is a 4D array
containing raw pixel data of the traffic sign images, (num examples,
width, height, channels).
\item
\texttt{\textquotesingle{}labels\textquotesingle{}} is a 1D array
containing the label/class id of the traffic sign. The file
\texttt{signnames.csv} contains id -\textgreater{} name mappings for
each id.
\item
\texttt{\textquotesingle{}sizes\textquotesingle{}} is a list
containing tuples, (width, height) representing the original width and
height the image.
\item
\texttt{\textquotesingle{}coords\textquotesingle{}} is a list
containing tuples, (x1, y1, x2, y2) representing coordinates of a
bounding box around the sign in the image. \textbf{THESE COORDINATES
ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS
(32 by 32) OF THESE IMAGES}
\end{itemize}
Complete the basic data summary below. Use python, numpy and/or pandas
methods to calculate the data summary rather than hard coding the
results. For example, the
\href{http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.shape.html}{pandas
shape method} might be useful for calculating some of the summary
results.
\hypertarget{provide-a-basic-summary-of-the-data-set-using-python-numpy-andor-pandas}{%
\subsubsection{Provide a Basic Summary of the Data Set Using Python,
Numpy and/or
Pandas}\label{provide-a-basic-summary-of-the-data-set-using-python-numpy-andor-pandas}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}226}]:} \PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} Replace each question mark with the appropriate value. }
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} Use python, pandas or numpy methods rather than hard coding the results}
\PY{c+c1}{\PYZsh{} TODO: Number of training examples}
\PY{n}{n\PYZus{}train} \PY{o}{=} \PY{n+nb}{len}\PY{p}{(}\PY{n}{y\PYZus{}train}\PY{p}{)}
\PY{c+c1}{\PYZsh{} TODO: Number of validation examples}
\PY{n}{n\PYZus{}validation} \PY{o}{=} \PY{n+nb}{len}\PY{p}{(}\PY{n}{y\PYZus{}valid}\PY{p}{)}
\PY{c+c1}{\PYZsh{} TODO: Number of testing examples.}
\PY{n}{n\PYZus{}test} \PY{o}{=} \PY{n+nb}{len}\PY{p}{(}\PY{n}{y\PYZus{}test}\PY{p}{)}
\PY{c+c1}{\PYZsh{} TODO: What\PYZsq{}s the shape of an traffic sign image?}
\PY{n}{image\PYZus{}shape} \PY{o}{=} \PY{n}{X\PYZus{}train}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{o}{.}\PY{n}{shape}
\PY{c+c1}{\PYZsh{} TODO: How many unique classes/labels there are in the dataset.}
\PY{n}{n\PYZus{}classes} \PY{o}{=} \PY{n+nb}{len}\PY{p}{(}\PY{n}{np}\PY{o}{.}\PY{n}{unique}\PY{p}{(}\PY{n}{y\PYZus{}train}\PY{p}{)}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Number of training examples =}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{n\PYZus{}train}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Number of validation examples =}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{n\PYZus{}validation}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Number of testing examples =}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{n\PYZus{}test}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Image data shape =}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{image\PYZus{}shape}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Number of classes =}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{n\PYZus{}classes}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
Number of training examples = 34799
Number of validation examples = 4410
Number of testing examples = 12630
Image data shape = (32, 32, 3)
Number of classes = 43
\end{Verbatim}
\hypertarget{include-an-exploratory-visualization-of-the-dataset}{%
\subsubsection{Include an exploratory visualization of the
dataset}\label{include-an-exploratory-visualization-of-the-dataset}}
Visualize the German Traffic Signs Dataset using the pickled file(s).
This is open ended, suggestions include: plotting traffic sign images,
plotting the count of each sign, etc.
The \href{http://matplotlib.org/}{Matplotlib}
\href{http://matplotlib.org/examples/index.html}{examples} and
\href{http://matplotlib.org/gallery.html}{gallery} pages are a great
resource for doing visualizations in Python.
\textbf{NOTE:} It's recommended you start with something simple first.
If you wish to do more, come back to it after you've completed the rest
of the sections. It can be interesting to look at the distribution of
classes in the training, validation and test set. Is the distribution
the same? Are there more examples of some classes than others?
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}227}]:} \PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} Data exploration visualization code goes here.}
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} Feel free to use as many code cells as needed.}
\PY{k+kn}{import} \PY{n+nn}{matplotlib}\PY{n+nn}{.}\PY{n+nn}{pyplot} \PY{k}{as} \PY{n+nn}{plt}
\PY{c+c1}{\PYZsh{} Visualizations will be shown in the notebook.}
\PY{o}{\PYZpc{}}\PY{k}{matplotlib} inline
\PY{k+kn}{import} \PY{n+nn}{csv}
\PY{c+c1}{\PYZsh{}Import traffic signs class names}
\PY{n}{signs\PYZus{}class}\PY{o}{=}\PY{p}{[}\PY{p}{]}
\PY{k}{with} \PY{n+nb}{open}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{signnames.csv}\PY{l+s+s1}{\PYZsq{}}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{rt}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)} \PY{k}{as} \PY{n}{csvfile}\PY{p}{:}
\PY{n}{reader} \PY{o}{=} \PY{n}{csv}\PY{o}{.}\PY{n}{DictReader}\PY{p}{(}\PY{n}{csvfile}\PY{p}{,} \PY{n}{delimiter}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{,}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{k}{for} \PY{n}{row} \PY{o+ow}{in} \PY{n}{reader}\PY{p}{:}
\PY{n}{signs\PYZus{}class}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{p}{(}\PY{n}{row}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{SignName}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}\PY{p}{)}
\PY{n}{len\PYZus{}samples}\PY{o}{=}\PY{p}{[}\PY{p}{]}
\PY{n}{plt}\PY{o}{.}\PY{n}{figure}\PY{p}{(}\PY{n}{figsize}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{15}\PY{p}{,}\PY{l+m+mi}{16}\PY{p}{)}\PY{p}{)}
\PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{,} \PY{n}{n\PYZus{}classes}\PY{p}{)}\PY{p}{:}
\PY{n}{plt}\PY{o}{.}\PY{n}{subplot}\PY{p}{(}\PY{l+m+mi}{10}\PY{p}{,} \PY{l+m+mi}{5}\PY{p}{,} \PY{n}{i}\PY{o}{+}\PY{l+m+mi}{1}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{axis}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{off}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{x\PYZus{}selected} \PY{o}{=} \PY{n}{X\PYZus{}train}\PY{p}{[}\PY{n}{y\PYZus{}train} \PY{o}{==} \PY{n}{i}\PY{p}{]}
\PY{n}{plt}\PY{o}{.}\PY{n}{imshow}\PY{p}{(}\PY{n}{x\PYZus{}selected}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{,} \PY{p}{:}\PY{p}{,} \PY{p}{:}\PY{p}{,} \PY{p}{:}\PY{p}{]}\PY{p}{)} \PY{c+c1}{\PYZsh{}draw the first image of each class}
\PY{n}{plt}\PY{o}{.}\PY{n}{title}\PY{p}{(}\PY{n}{signs\PYZus{}class}\PY{p}{[}\PY{n}{i}\PY{p}{]}\PY{p}{)}
\PY{n}{len\PYZus{}samples}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n+nb}{len}\PY{p}{(}\PY{n}{x\PYZus{}selected}\PY{p}{)}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{show}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{}Plot number of images per class}
\PY{n}{plt}\PY{o}{.}\PY{n}{figure}\PY{p}{(}\PY{n}{figsize}\PY{o}{=}\PY{p}{(}\PY{l+m+mi}{16}\PY{p}{,} \PY{l+m+mi}{4}\PY{p}{)}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{bar}\PY{p}{(}\PY{n+nb}{range}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{,} \PY{n}{n\PYZus{}classes}\PY{p}{)}\PY{p}{,} \PY{n}{len\PYZus{}samples}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{title}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Distribution graph}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{xlabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Class label}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{ylabel}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Number of images}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}
\PY{n}{plt}\PY{o}{.}\PY{n}{show}\PY{p}{(}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Minimum number of images for each label =}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n+nb}{min}\PY{p}{(}\PY{n}{len\PYZus{}samples}\PY{p}{)}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{Maximum number of images for each label =}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n+nb}{max}\PY{p}{(}\PY{n}{len\PYZus{}samples}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_9_0.png}
\end{center}
{ \hspace*{\fill} \\}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_9_1.png}
\end{center}
{ \hspace*{\fill} \\}
\begin{Verbatim}[commandchars=\\\{\}]
Minimum number of images for each label = 180
Maximum number of images for each label = 2010
\end{Verbatim}
\begin{center}\rule{0.5\linewidth}{\linethickness}\end{center}
\hypertarget{step-2-design-and-test-a-model-architecture}{%
\subsection{Step 2: Design and Test a Model
Architecture}\label{step-2-design-and-test-a-model-architecture}}
Design and implement a deep learning model that learns to recognize
traffic signs. Train and test your model on the
\href{http://benchmark.ini.rub.de/?section=gtsrb\&subsection=dataset}{German
Traffic Sign Dataset}.
The LeNet-5 implementation shown in the
\href{https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81}{classroom}
at the end of the CNN lesson is a solid starting point. You'll have to
change the number of classes and possibly the preprocessing, but aside
from that it's plug and play!
With the LeNet-5 solution from the lecture, you should expect a
validation set accuracy of about 0.89. To meet specifications, the
validation set accuracy will need to be at least 0.93. It is possible to
get an even higher accuracy, but 0.93 is the minimum for a successful
project submission.
There are various aspects to consider when thinking about this problem:
\begin{itemize}
\tightlist
\item
Neural network architecture (is the network over or underfitting?)
\item
Play around preprocessing techniques (normalization, rgb to grayscale,
etc)
\item
Number of examples per label (some have more than others).
\item
Generate fake data.
\end{itemize}
Here is an example of a
\href{http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf}{published
baseline model on this problem}. It's not required to be familiar with
the approach used in the paper but, it's good practice to try to read
papers like these.
\hypertarget{pre-process-the-data-set-normalization-grayscale-etc.}{%
\subsubsection{Pre-process the Data Set (normalization, grayscale,
etc.)}\label{pre-process-the-data-set-normalization-grayscale-etc.}}
Minimally, the image data should be normalized so that the data has mean
zero and equal variance. For image data, \texttt{(pixel\ -\ 128)/\ 128}
is a quick way to approximately normalize the data and can be used in
this project.
Other pre-processing steps are optional. You can try different
techniques to see if it improves performance.
Use the code cell (or multiple code cells, if necessary) to implement
the first step of your project.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}228}]:} \PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} Preprocess the data here. It is required to normalize the data. Other preprocessing steps could include }
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} converting to grayscale, etc.}
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} Feel free to use as many code cells as needed.}
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}
\PY{k+kn}{import} \PY{n+nn}{cv2}
\PY{k+kn}{import} \PY{n+nn}{matplotlib}\PY{n+nn}{.}\PY{n+nn}{pyplot} \PY{k}{as} \PY{n+nn}{plt}
\PY{k+kn}{from} \PY{n+nn}{sklearn}\PY{n+nn}{.}\PY{n+nn}{utils} \PY{k}{import} \PY{n}{shuffle}
\PY{c+c1}{\PYZsh{} 1. Normalize image data X}
\PY{c+c1}{\PYZsh{} 2. Shuffle data to prevent ordered learning }
\PY{c+c1}{\PYZsh{} 3. Convert to one hot later ikn the training section with tf.one\PYZus{}hot}
\PY{k}{def} \PY{n+nf}{preprocess\PYZus{}data}\PY{p}{(}\PY{n}{X}\PY{p}{,} \PY{n}{y}\PY{p}{)}\PY{p}{:}
\PY{c+c1}{\PYZsh{} Mean normalizes the pixel and make all image array values fall within the range \PYZhy{}1 to 1}
\PY{c+c1}{\PYZsh{} Note all values in original images are between 0 and 255, as uint8}
\PY{n}{X} \PY{o}{=} \PY{n}{X}\PY{o}{.}\PY{n}{astype}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{float32}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{X} \PY{o}{=} \PY{p}{(}\PY{n}{X} \PY{o}{\PYZhy{}} \PY{l+m+mf}{128.}\PY{p}{)} \PY{o}{/} \PY{l+m+mf}{128.}
\PY{c+c1}{\PYZsh{} DATA SHUFFLE}
\PY{n}{shuffled\PYZus{}X}\PY{p}{,} \PY{n}{shuffled\PYZus{}y} \PY{o}{=} \PY{n}{shuffle}\PY{p}{(}\PY{n}{X}\PY{p}{,} \PY{n}{y}\PY{p}{)}
\PY{k}{return} \PY{n}{shuffled\PYZus{}X}\PY{p}{,} \PY{n}{shuffled\PYZus{}y}
\PY{c+c1}{\PYZsh{} PRE \PYZhy{} PROCESS DATA}
\PY{k}{if} \PY{n}{PRE\PYZus{}PROCESSING\PYZus{}DONE}\PY{o}{==}\PY{k+kc}{False}\PY{p}{:}
\PY{n}{X\PYZus{}train}\PY{p}{,} \PY{n}{y\PYZus{}train} \PY{o}{=} \PY{n}{preprocess\PYZus{}data}\PY{p}{(}\PY{n}{X\PYZus{}train}\PY{p}{,} \PY{n}{y\PYZus{}train}\PY{p}{)}
\PY{n}{X\PYZus{}valid}\PY{p}{,} \PY{n}{y\PYZus{}valid} \PY{o}{=} \PY{n}{preprocess\PYZus{}data}\PY{p}{(}\PY{n}{X\PYZus{}valid}\PY{p}{,} \PY{n}{y\PYZus{}valid}\PY{p}{)}
\PY{n}{X\PYZus{}test}\PY{p}{,} \PY{n}{y\PYZus{}test} \PY{o}{=} \PY{n}{preprocess\PYZus{}data}\PY{p}{(}\PY{n}{X\PYZus{}test}\PY{p}{,} \PY{n}{y\PYZus{}test}\PY{p}{)}
\PY{n}{PRE\PYZus{}PROCESSING\PYZus{}DONE} \PY{o}{=} \PY{k+kc}{True}
\PY{c+c1}{\PYZsh{} SHOW AND PRINT PRE PROCESSED AND SHUFFLED VALUE}
\PY{n}{plt}\PY{o}{.}\PY{n}{imshow}\PY{p}{(}\PY{n}{X\PYZus{}train}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{X\PYZus{}train}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]} \PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+se}{\PYZbs{}n}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{signs\PYZus{}class}\PY{p}{[}\PY{n}{y\PYZus{}train}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{]}\PY{p}{,} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZhy{} Index: }\PY{l+s+si}{\PYZob{}\PYZcb{}}\PY{l+s+s1}{\PYZsq{}}\PY{o}{.}\PY{n}{format}\PY{p}{(}\PY{n}{y\PYZus{}train}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
[[[-0.57031 -0.53906 -0.47656]
[-0.61719 -0.59375 -0.53125]
[-0.61719 -0.60938 -0.60938]
{\ldots},
[-0.44531 -0.48438 -0.44531]
[-0.375 -0.375 -0.32812]
[-0.47656 -0.42969 -0.36719]]
[[-0.60156 -0.60156 -0.55469]
[-0.625 -0.625 -0.57031]
[-0.625 -0.625 -0.64062]
{\ldots},
[-0.48438 -0.5 -0.46875]
[-0.46875 -0.46875 -0.42188]
[-0.48438 -0.46094 -0.40625]]
[[-0.64062 -0.64062 -0.59375]
[-0.64844 -0.64844 -0.60156]
[-0.625 -0.64062 -0.64062]
{\ldots},
[-0.52344 -0.52344 -0.49219]
[-0.53906 -0.53906 -0.49219]
[-0.52344 -0.51562 -0.46875]]
{\ldots},
[[-0.25781 -0.32031 -0.42969]
[-0.28906 -0.34375 -0.42969]
[-0.33594 -0.375 -0.47656]
{\ldots},
[-0.23438 -0.23438 -0.20312]
[-0.23438 -0.22656 -0.17188]
[-0.23438 -0.21875 -0.16406]]
[[-0.375 -0.38281 -0.47656]
[-0.39062 -0.39844 -0.45312]
[-0.375 -0.375 -0.44531]
{\ldots},
[-0.20312 -0.17188 -0.10938]
[-0.22656 -0.1875 -0.10938]
[-0.23438 -0.1875 -0.11719]]
[[-0.36719 -0.35938 -0.39844]
[-0.375 -0.36719 -0.375 ]
[-0.375 -0.36719 -0.375 ]
{\ldots},
[-0.21094 -0.17969 -0.11719]
[-0.23438 -0.20312 -0.13281]
[-0.23438 -0.19531 -0.125 ]]]
Keep right - Index: 38
\end{Verbatim}
\begin{center}
\adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_13_1.png}
\end{center}
{ \hspace*{\fill} \\}
\hypertarget{tensorflow-setup-and-gpu-check}{%
\subsection{Tensorflow Setup and GPU
Check}\label{tensorflow-setup-and-gpu-check}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}229}]:} \PY{c+c1}{\PYZsh{} PRE\PYZhy{} REQUISITES : Download carnd\PYZhy{}term1 with gpu enabled , }
\PY{c+c1}{\PYZsh{} Remove existing and install with condo with command : conda env create \PYZhy{}f environment\PYZhy{}gpu.yml}
\PY{c+c1}{\PYZsh{} TENSORFLOW RELEASE INFORMATION OFFICIAL}
\PY{c+c1}{\PYZsh{} All our prebuilt binaries have been built with CUDA 8 and cuDNN 6.}
\PY{c+c1}{\PYZsh{} We anticipate releasing TensorFlow 1.5 with CUDA 9 and cuDNN 7.}
\PY{c+c1}{\PYZsh{} https://devtalk.nvidia.com/default/topic/1026198/cuda\PYZhy{}9\PYZhy{}0\PYZhy{}importerror\PYZhy{}libcublas\PYZhy{}so\PYZhy{}8\PYZhy{}0/}
\PY{k+kn}{import} \PY{n+nn}{tensorflow} \PY{k}{as} \PY{n+nn}{tf}
\PY{c+c1}{\PYZsh{} Check for a GPU}
\PY{k}{if} \PY{o+ow}{not} \PY{n}{tf}\PY{o}{.}\PY{n}{test}\PY{o}{.}\PY{n}{gpu\PYZus{}device\PYZus{}name}\PY{p}{(}\PY{p}{)}\PY{p}{:}
\PY{n}{warnings}\PY{o}{.}\PY{n}{warn}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{No GPU found. Please use a GPU to train your neural network.}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{k}{else}\PY{p}{:}
\PY{n+nb}{print}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Default GPU Device: }\PY{l+s+si}{\PYZob{}\PYZcb{}}\PY{l+s+s1}{\PYZsq{}}\PY{o}{.}\PY{n}{format}\PY{p}{(}\PY{n}{tf}\PY{o}{.}\PY{n}{test}\PY{o}{.}\PY{n}{gpu\PYZus{}device\PYZus{}name}\PY{p}{(}\PY{p}{)}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-229-b1fe6d710a93> in <module>()
10 import tensorflow as tf
11 \# Check for a GPU
---> 12 if not tf.test.gpu\_device\_name():
13 warnings.warn('No GPU found. Please use a GPU to train your neural network.')
14 else:
AttributeError: module 'tensorflow.python.platform.test' has no attribute 'gpu\_device\_name'
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor} }]:} \PY{k+kn}{import} \PY{n+nn}{tensorflow} \PY{k}{as} \PY{n+nn}{tf}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{tf}\PY{o}{.}\PY{n}{\PYZus{}\PYZus{}version\PYZus{}\PYZus{}}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Creates a graph.}
\PY{n}{a} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{constant}\PY{p}{(}\PY{p}{[}\PY{l+m+mf}{1.0}\PY{p}{,} \PY{l+m+mf}{2.0}\PY{p}{,} \PY{l+m+mf}{3.0}\PY{p}{,} \PY{l+m+mf}{4.0}\PY{p}{,} \PY{l+m+mf}{5.0}\PY{p}{,} \PY{l+m+mf}{6.0}\PY{p}{]}\PY{p}{,} \PY{n}{shape}\PY{o}{=}\PY{p}{[}\PY{l+m+mi}{2}\PY{p}{,} \PY{l+m+mi}{3}\PY{p}{]}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{a}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{b} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{constant}\PY{p}{(}\PY{p}{[}\PY{l+m+mf}{1.0}\PY{p}{,} \PY{l+m+mf}{2.0}\PY{p}{,} \PY{l+m+mf}{3.0}\PY{p}{,} \PY{l+m+mf}{4.0}\PY{p}{,} \PY{l+m+mf}{5.0}\PY{p}{,} \PY{l+m+mf}{6.0}\PY{p}{]}\PY{p}{,} \PY{n}{shape}\PY{o}{=}\PY{p}{[}\PY{l+m+mi}{3}\PY{p}{,} \PY{l+m+mi}{2}\PY{p}{]}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{b}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{n}{c} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{matmul}\PY{p}{(}\PY{n}{a}\PY{p}{,} \PY{n}{b}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Creates a session with log\PYZus{}device\PYZus{}placement set to True.}
\PY{n}{sess} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{Session}\PY{p}{(}\PY{n}{config}\PY{o}{=}\PY{n}{tf}\PY{o}{.}\PY{n}{ConfigProto}\PY{p}{(}\PY{n}{log\PYZus{}device\PYZus{}placement}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)}\PY{p}{)}
\PY{c+c1}{\PYZsh{} Runs the op.}
\PY{n+nb}{print}\PY{p}{(}\PY{n}{sess}\PY{o}{.}\PY{n}{run}\PY{p}{(}\PY{n}{c}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\hypertarget{model-architecture}{%
\subsection{Model Architecture}\label{model-architecture}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}230}]:} \PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} Define your architecture here.}
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} Feel free to use as many code cells as needed.}
\PY{k+kn}{import} \PY{n+nn}{tensorflow} \PY{k}{as} \PY{n+nn}{tf}
\PY{n}{tf}\PY{o}{.}\PY{n}{reset\PYZus{}default\PYZus{}graph}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{} With LeNet1\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}}
\PY{c+c1}{\PYZsh{} Accuracy below is dependent on shuffling as well}
\PY{c+c1}{\PYZsh{}Model Parameters Set}
\PY{c+c1}{\PYZsh{} EPOCHS = 15}
\PY{c+c1}{\PYZsh{} BATCH\PYZus{}SIZE = 128}
\PY{c+c1}{\PYZsh{} learn\PYZus{}rate = 0.001}
\PY{c+c1}{\PYZsh{} \PYZsh{} accuracy of 94.6}
\PY{c+c1}{\PYZsh{} OR}
\PY{c+c1}{\PYZsh{} EPOCHS = 15}
\PY{c+c1}{\PYZsh{} BATCH\PYZus{}SIZE = 64}
\PY{c+c1}{\PYZsh{} \PYZsh{} accuracy of 94.2}
\PY{c+c1}{\PYZsh{} OR}
\PY{n}{EPOCHS} \PY{o}{=} \PY{l+m+mi}{40}
\PY{n}{BATCH\PYZus{}SIZE} \PY{o}{=} \PY{l+m+mi}{64}
\PY{c+c1}{\PYZsh{} accuracy of 96.1}
\PY{c+c1}{\PYZsh{} Test accuracy of 95.1}
\PY{c+c1}{\PYZsh{} \PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}\PYZhy{}}
\PY{n}{LEARN\PYZus{}RATE} \PY{o}{=} \PY{l+m+mf}{0.001}
\PY{n}{MEAN} \PY{o}{=} \PY{l+m+mf}{0.}
\PY{n}{STD\PYZus{}DEV} \PY{o}{=} \PY{l+m+mf}{0.1}
\PY{n}{DROPOUT} \PY{o}{=} \PY{l+m+mf}{0.5}
\end{Verbatim}
\hypertarget{lenet-architecture}{%
\subsection{LeNet Architecture}\label{lenet-architecture}}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}231}]:} \PY{c+c1}{\PYZsh{}Function for Initializing Random TF Variables}
\PY{k}{def} \PY{n+nf}{trun\PYZus{}var}\PY{p}{(}\PY{n}{dim}\PY{o}{=}\PY{p}{[}\PY{p}{]}\PY{p}{,} \PY{n}{mean}\PY{o}{=}\PY{n}{MEAN}\PY{p}{,} \PY{n}{std}\PY{o}{=}\PY{n}{STD\PYZus{}DEV}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{k+kc}{None}\PY{p}{)}\PY{p}{:}
\PY{k}{return} \PY{n}{tf}\PY{o}{.}\PY{n}{Variable}\PY{p}{(}\PY{n}{tf}\PY{o}{.}\PY{n}{truncated\PYZus{}normal}\PY{p}{(}\PY{n}{dim}\PY{p}{,} \PY{n}{mean}\PY{o}{=}\PY{n}{mean}\PY{p}{,} \PY{n}{stddev}\PY{o}{=}\PY{n}{std}\PY{p}{)}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{n}{name}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}232}]:} \PY{k}{def} \PY{n+nf}{LeNet}\PY{p}{(}\PY{n}{x}\PY{p}{,} \PY{n}{keep\PYZus{}prob}\PY{p}{)}\PY{p}{:}
\PY{c+c1}{\PYZsh{}Initializing Weights and Biases}
\PY{n}{weights} \PY{o}{=} \PY{p}{\PYZob{}} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{conv1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:} \PY{n}{trun\PYZus{}var}\PY{p}{(}\PY{p}{[}\PY{l+m+mi}{5}\PY{p}{,}\PY{l+m+mi}{5}\PY{p}{,}\PY{l+m+mi}{3}\PY{p}{,}\PY{l+m+mi}{6}\PY{p}{]}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{weights\PYZus{}conv1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{conv2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:} \PY{n}{trun\PYZus{}var}\PY{p}{(}\PY{p}{[}\PY{l+m+mi}{5}\PY{p}{,}\PY{l+m+mi}{5}\PY{p}{,}\PY{l+m+mi}{6}\PY{p}{,}\PY{l+m+mi}{16}\PY{p}{]}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{weights\PYZus{}conv2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{full\PYZus{}1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:} \PY{n}{trun\PYZus{}var}\PY{p}{(}\PY{p}{[}\PY{l+m+mi}{400}\PY{p}{,}\PY{l+m+mi}{120}\PY{p}{]}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{weights\PYZus{}full\PYZus{}1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{full\PYZus{}2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:} \PY{n}{trun\PYZus{}var}\PY{p}{(}\PY{p}{[}\PY{l+m+mi}{120}\PY{p}{,}\PY{l+m+mi}{83}\PY{p}{]}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{weights\PYZus{}full\PYZus{}2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{out}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:} \PY{n}{trun\PYZus{}var}\PY{p}{(}\PY{p}{[}\PY{l+m+mi}{83}\PY{p}{,} \PY{n}{n\PYZus{}classes}\PY{p}{]}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{weights\PYZus{}out}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{\PYZcb{}}
\PY{n}{biases} \PY{o}{=} \PY{p}{\PYZob{}} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{conv1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:} \PY{n}{trun\PYZus{}var}\PY{p}{(}\PY{p}{[}\PY{l+m+mi}{6}\PY{p}{]}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{biases\PYZus{}conv1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{conv2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:} \PY{n}{trun\PYZus{}var}\PY{p}{(}\PY{p}{[}\PY{l+m+mi}{16}\PY{p}{]}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{biases\PYZus{}conv2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{full\PYZus{}1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:} \PY{n}{trun\PYZus{}var}\PY{p}{(}\PY{p}{[}\PY{l+m+mi}{120}\PY{p}{]}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{biases\PYZus{}full\PYZus{}1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{full\PYZus{}2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:} \PY{n}{trun\PYZus{}var}\PY{p}{(}\PY{p}{[}\PY{l+m+mi}{83}\PY{p}{]}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{biases\PYZus{}full\PYZus{}2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{,}
\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{out}\PY{l+s+s1}{\PYZsq{}}\PY{p}{:} \PY{n}{trun\PYZus{}var}\PY{p}{(}\PY{p}{[}\PY{n}{n\PYZus{}classes}\PY{p}{]}\PY{p}{,} \PY{n}{name}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{biases\PYZus{}out}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{\PYZcb{}}
\PY{c+c1}{\PYZsh{}LeNet CNN Model Implementation}
\PY{c+c1}{\PYZsh{}Convolutional Layer 1}
\PY{c+c1}{\PYZsh{}INPUT 32x32x1}
\PY{n}{conv1} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{conv2d}\PY{p}{(}\PY{n}{x}\PY{p}{,} \PY{n}{weights}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{conv1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,} \PY{n}{strides}\PY{o}{=}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{,} \PY{n}{padding}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{VALID}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{c+c1}{\PYZsh{}28x28x6}
\PY{n}{conv1} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{bias\PYZus{}add}\PY{p}{(}\PY{n}{conv1}\PY{p}{,} \PY{n}{biases}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{conv1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}
\PY{n}{conv1} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{relu}\PY{p}{(}\PY{n}{conv1}\PY{p}{)}
\PY{n}{conv1} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{max\PYZus{}pool}\PY{p}{(}\PY{n}{conv1}\PY{p}{,} \PY{n}{ksize}\PY{o}{=}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{2}\PY{p}{,}\PY{l+m+mi}{2}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{,} \PY{n}{strides}\PY{o}{=}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{2}\PY{p}{,}\PY{l+m+mi}{2}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{,} \PY{n}{padding}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{SAME}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{c+c1}{\PYZsh{}14x14x6}
\PY{c+c1}{\PYZsh{}Convolutional Layer 2}
\PY{n}{conv2} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{conv2d}\PY{p}{(}\PY{n}{conv1}\PY{p}{,} \PY{n}{weights}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{conv2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{,} \PY{n}{strides}\PY{o}{=}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{,} \PY{n}{padding}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{VALID}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{c+c1}{\PYZsh{}10x10x16}
\PY{n}{conv2} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{bias\PYZus{}add}\PY{p}{(}\PY{n}{conv2}\PY{p}{,} \PY{n}{biases}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{conv2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}
\PY{c+c1}{\PYZsh{} USED IN OPTIONAL 5 SECTION}
\PY{n}{CONV2\PYZus{}BIAS} \PY{o}{=} \PY{n}{conv2}
\PY{n}{conv2} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{relu}\PY{p}{(}\PY{n}{conv2}\PY{p}{)}
\PY{n}{conv2} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{max\PYZus{}pool}\PY{p}{(}\PY{n}{conv2}\PY{p}{,} \PY{n}{ksize}\PY{o}{=}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{2}\PY{p}{,}\PY{l+m+mi}{2}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{,} \PY{n}{strides}\PY{o}{=}\PY{p}{[}\PY{l+m+mi}{1}\PY{p}{,}\PY{l+m+mi}{2}\PY{p}{,}\PY{l+m+mi}{2}\PY{p}{,}\PY{l+m+mi}{1}\PY{p}{]}\PY{p}{,} \PY{n}{padding}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{SAME}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}
\PY{c+c1}{\PYZsh{}5x5x16}
\PY{c+c1}{\PYZsh{}Flatten}
\PY{n}{conv2} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{contrib}\PY{o}{.}\PY{n}{layers}\PY{o}{.}\PY{n}{flatten}\PY{p}{(}\PY{n}{conv2}\PY{p}{)}
\PY{c+c1}{\PYZsh{}400}
\PY{c+c1}{\PYZsh{}Fully Connected Layer 1}
\PY{n}{full\PYZus{}1} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{add}\PY{p}{(}\PY{n}{tf}\PY{o}{.}\PY{n}{matmul}\PY{p}{(}\PY{n}{conv2}\PY{p}{,} \PY{n}{weights}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{full\PYZus{}1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}\PY{p}{,} \PY{n}{biases}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{full\PYZus{}1}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}
\PY{c+c1}{\PYZsh{}120}
\PY{n}{full\PYZus{}1} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{relu}\PY{p}{(}\PY{n}{full\PYZus{}1}\PY{p}{)}
\PY{n}{full\PYZus{}1} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{dropout}\PY{p}{(}\PY{n}{full\PYZus{}1}\PY{p}{,} \PY{n}{keep\PYZus{}prob}\PY{p}{)}
\PY{c+c1}{\PYZsh{}Fully Connected Layer 2}
\PY{n}{full\PYZus{}2} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{add}\PY{p}{(}\PY{n}{tf}\PY{o}{.}\PY{n}{matmul}\PY{p}{(}\PY{n}{full\PYZus{}1}\PY{p}{,} \PY{n}{weights}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{full\PYZus{}2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}\PY{p}{,} \PY{n}{biases}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{full\PYZus{}2}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}
\PY{c+c1}{\PYZsh{}83}
\PY{n}{full\PYZus{}2} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{relu}\PY{p}{(}\PY{n}{full\PYZus{}2}\PY{p}{)}
\PY{n}{full\PYZus{}2} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{dropout}\PY{p}{(}\PY{n}{full\PYZus{}2}\PY{p}{,} \PY{n}{keep\PYZus{}prob}\PY{p}{)}
\PY{c+c1}{\PYZsh{}Output Layer}
\PY{n}{logits} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{add}\PY{p}{(}\PY{n}{tf}\PY{o}{.}\PY{n}{matmul}\PY{p}{(}\PY{n}{full\PYZus{}2}\PY{p}{,} \PY{n}{weights}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{out}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}\PY{p}{,} \PY{n}{biases}\PY{p}{[}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{out}\PY{l+s+s1}{\PYZsq{}}\PY{p}{]}\PY{p}{)}
\PY{k}{return} \PY{n}{logits}\PY{p}{,} \PY{n}{CONV2\PYZus{}BIAS}
\PY{c+c1}{\PYZsh{}N\PYZus{}CLASSES = 43}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}233}]:} \PY{c+c1}{\PYZsh{} TODO}
\PY{c+c1}{\PYZsh{} def LeNet2(x, keep\PYZus{}prob): }
\PY{c+c1}{\PYZsh{} \PYZsh{}Initializing Weights and Biases}
\PY{c+c1}{\PYZsh{} weights = \PYZob{} \PYZsq{}conv1\PYZsq{}: trun\PYZus{}var([3,3,3,12], name=\PYZsq{}weights\PYZus{}conv1\PYZsq{}),}
\PY{c+c1}{\PYZsh{} \PYZsq{}conv2\PYZsq{}: trun\PYZus{}var([3,3,12,24], name=\PYZsq{}weights\PYZus{}conv2\PYZsq{}),}
\PY{c+c1}{\PYZsh{} \PYZsq{}full\PYZus{}1\PYZsq{}: trun\PYZus{}var([600,353], name=\PYZsq{}weights\PYZus{}full\PYZus{}1\PYZsq{}),}
\PY{c+c1}{\PYZsh{} \PYZsq{}full\PYZus{}2\PYZsq{}: trun\PYZus{}var([353,83], name=\PYZsq{}weights\PYZus{}full\PYZus{}2\PYZsq{}),}
\PY{c+c1}{\PYZsh{} \PYZsq{}out\PYZsq{}: trun\PYZus{}var([83, n\PYZus{}classes], name=\PYZsq{}weights\PYZus{}out\PYZsq{})\PYZcb{}}
\PY{c+c1}{\PYZsh{} biases = \PYZob{} \PYZsq{}conv1\PYZsq{}: trun\PYZus{}var([12], name=\PYZsq{}biases\PYZus{}conv1\PYZsq{}),}
\PY{c+c1}{\PYZsh{} \PYZsq{}conv2\PYZsq{}: trun\PYZus{}var([24], name=\PYZsq{}biases\PYZus{}conv2\PYZsq{}),}
\PY{c+c1}{\PYZsh{} \PYZsq{}full\PYZus{}1\PYZsq{}: trun\PYZus{}var([353], name=\PYZsq{}biases\PYZus{}full\PYZus{}1\PYZsq{}),}
\PY{c+c1}{\PYZsh{} \PYZsq{}full\PYZus{}2\PYZsq{}: trun\PYZus{}var([83], name=\PYZsq{}biases\PYZus{}full\PYZus{}2\PYZsq{}),}
\PY{c+c1}{\PYZsh{} \PYZsq{}out\PYZsq{}: trun\PYZus{}var([n\PYZus{}classes], name=\PYZsq{}biases\PYZus{}out\PYZsq{})\PYZcb{}}
\PY{c+c1}{\PYZsh{} \PYZsh{}LeNet CNN Model Implementation}
\PY{c+c1}{\PYZsh{} \PYZsh{}Convolutional Layer 1}
\PY{c+c1}{\PYZsh{} \PYZsh{}INPUT 32x32x1}
\PY{c+c1}{\PYZsh{} conv1 = tf.nn.conv2d(x, weights[\PYZsq{}conv1\PYZsq{}], strides=[1,1,1,1], padding=\PYZsq{}VALID\PYZsq{}) }
\PY{c+c1}{\PYZsh{} \PYZsh{}28x28x12}
\PY{c+c1}{\PYZsh{} conv1 = tf.nn.bias\PYZus{}add(conv1, biases[\PYZsq{}conv1\PYZsq{}])}
\PY{c+c1}{\PYZsh{} conv1 = tf.nn.relu(conv1)}
\PY{c+c1}{\PYZsh{} conv1 = tf.nn.max\PYZus{}pool(conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding=\PYZsq{}SAME\PYZsq{}) }
\PY{c+c1}{\PYZsh{} \PYZsh{}14x14x24}
\PY{c+c1}{\PYZsh{} \PYZsh{}Convolutional Layer 2}
\PY{c+c1}{\PYZsh{} conv2 = tf.nn.conv2d(conv1, weights[\PYZsq{}conv2\PYZsq{}], strides=[1,1,1,1], padding=\PYZsq{}VALID\PYZsq{})}
\PY{c+c1}{\PYZsh{} \PYZsh{}10x10x24}
\PY{c+c1}{\PYZsh{} conv2 = tf.nn.bias\PYZus{}add(conv2, biases[\PYZsq{}conv2\PYZsq{}])}
\PY{c+c1}{\PYZsh{} \PYZsh{} USED IN OPTIONAL 5 SECTION}
\PY{c+c1}{\PYZsh{} CONV2\PYZus{}BIAS = conv2}
\PY{c+c1}{\PYZsh{} conv2 = tf.nn.relu(conv2)}
\PY{c+c1}{\PYZsh{} conv2 = tf.nn.max\PYZus{}pool(conv2, ksize=[1,2,2,1], strides=[1,2,2,1], padding=\PYZsq{}SAME\PYZsq{}) }
\PY{c+c1}{\PYZsh{} \PYZsh{}5x5x24}
\PY{c+c1}{\PYZsh{} \PYZsh{}Flatten}
\PY{c+c1}{\PYZsh{} conv2 = tf.contrib.layers.flatten(conv2)}
\PY{c+c1}{\PYZsh{} \PYZsh{}600}
\PY{c+c1}{\PYZsh{} \PYZsh{}Fully Connected Layer 1}
\PY{c+c1}{\PYZsh{} full\PYZus{}1 = tf.add(tf.matmul(conv2, weights[\PYZsq{}full\PYZus{}1\PYZsq{}]), biases[\PYZsq{}full\PYZus{}1\PYZsq{}]) }
\PY{c+c1}{\PYZsh{} \PYZsh{}353}
\PY{c+c1}{\PYZsh{} full\PYZus{}1 = tf.nn.relu(full\PYZus{}1)}
\PY{c+c1}{\PYZsh{} full\PYZus{}1 = tf.nn.dropout(full\PYZus{}1, keep\PYZus{}prob)}
\PY{c+c1}{\PYZsh{} \PYZsh{}Fully Connected Layer 2}
\PY{c+c1}{\PYZsh{} full\PYZus{}2 = tf.add(tf.matmul(full\PYZus{}1, weights[\PYZsq{}full\PYZus{}2\PYZsq{}]), biases[\PYZsq{}full\PYZus{}2\PYZsq{}]) }
\PY{c+c1}{\PYZsh{} \PYZsh{}83}
\PY{c+c1}{\PYZsh{} full\PYZus{}2 = tf.nn.relu(full\PYZus{}2)}
\PY{c+c1}{\PYZsh{} full\PYZus{}2 = tf.nn.dropout(full\PYZus{}2, keep\PYZus{}prob)}
\PY{c+c1}{\PYZsh{} \PYZsh{}Output Layer}
\PY{c+c1}{\PYZsh{} logits = tf.add(tf.matmul(full\PYZus{}2, weights[\PYZsq{}out\PYZsq{}]), biases[\PYZsq{}out\PYZsq{}]) }
\PY{c+c1}{\PYZsh{} return logits, CONV2\PYZus{}BIAS}
\PY{c+c1}{\PYZsh{} \PYZsh{}N\PYZus{}CLASSES = 43}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}234}]:} \PY{c+c1}{\PYZsh{}Placeholders for Network}
\PY{n}{x} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{placeholder}\PY{p}{(}\PY{n}{tf}\PY{o}{.}\PY{n}{float32}\PY{p}{,} \PY{p}{[}\PY{k+kc}{None}\PY{p}{,} \PY{l+m+mi}{32}\PY{p}{,}\PY{l+m+mi}{32}\PY{p}{,}\PY{l+m+mi}{3}\PY{p}{]}\PY{p}{)} \PY{c+c1}{\PYZsh{}Colored}
\PY{n}{y} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{placeholder}\PY{p}{(}\PY{n}{tf}\PY{o}{.}\PY{n}{int32}\PY{p}{,} \PY{p}{[}\PY{k+kc}{None}\PY{p}{]}\PY{p}{)}
\PY{n}{one\PYZus{}hot\PYZus{}y} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{one\PYZus{}hot}\PY{p}{(}\PY{n}{y}\PY{p}{,} \PY{n}{n\PYZus{}classes}\PY{p}{)}
\PY{n}{labels} \PY{o}{=} \PY{n}{one\PYZus{}hot\PYZus{}y}
\PY{n}{keep\PYZus{}prob} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{placeholder}\PY{p}{(}\PY{n}{tf}\PY{o}{.}\PY{n}{float32}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}235}]:} \PY{c+c1}{\PYZsh{}\PYZsh{} Training}
\PY{n}{logits}\PY{p}{,} \PY{n}{CONV2\PYZus{}BIAS} \PY{o}{=} \PY{n}{LeNet}\PY{p}{(}\PY{n}{x}\PY{p}{,}\PY{n}{keep\PYZus{}prob}\PY{p}{)}
\PY{c+c1}{\PYZsh{} logits = LeNet2(x,keep\PYZus{}prob)}
\PY{c+c1}{\PYZsh{}Cost Function}
\PY{n}{cost} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{nn}\PY{o}{.}\PY{n}{softmax\PYZus{}cross\PYZus{}entropy\PYZus{}with\PYZus{}logits}\PY{p}{(}\PY{n}{logits}\PY{o}{=}\PY{n}{logits}\PY{p}{,} \PY{n}{labels}\PY{o}{=}\PY{n}{labels}\PY{p}{)}
\PY{c+c1}{\PYZsh{}Adam Optimizer for Back Propagation}
\PY{n}{optimizer} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{train}\PY{o}{.}\PY{n}{AdamOptimizer}\PY{p}{(}\PY{n}{learning\PYZus{}rate}\PY{o}{=}\PY{n}{LEARN\PYZus{}RATE}\PY{p}{)}\PY{o}{.}\PY{n}{minimize}\PY{p}{(}\PY{n}{cost}\PY{p}{)}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}236}]:} \PY{c+c1}{\PYZsh{}\PYZsh{} Prediction and Accuracy}
\PY{n}{correct\PYZus{}prediction} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{equal}\PY{p}{(}\PY{n}{tf}\PY{o}{.}\PY{n}{argmax}\PY{p}{(}\PY{n}{labels}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{)}\PY{p}{,} \PY{n}{tf}\PY{o}{.}\PY{n}{argmax}\PY{p}{(}\PY{n}{logits}\PY{p}{,} \PY{l+m+mi}{1}\PY{p}{)}\PY{p}{)}
\PY{n}{accuracy} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{reduce\PYZus{}mean}\PY{p}{(}\PY{n}{tf}\PY{o}{.}\PY{n}{cast}\PY{p}{(}\PY{n}{correct\PYZus{}prediction}\PY{p}{,} \PY{n}{tf}\PY{o}{.}\PY{n}{float32}\PY{p}{)}\PY{p}{)}
\end{Verbatim}
\hypertarget{train-validate-and-test-the-model}{%
\subsubsection{Train, Validate and Test the
Model}\label{train-validate-and-test-the-model}}
A validation set can be used to assess how well the model is performing.
A low accuracy on the training and validation sets imply underfitting. A
high accuracy on the training set but low accuracy on the validation set
implies overfitting.
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}237}]:} \PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} Train your model here.}
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} Calculate and report the accuracy on the training and validation set.}
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} Once a final model architecture is selected, }
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} the accuracy on the test set should be calculated and reported as well.}
\PY{c+c1}{\PYZsh{}\PYZsh{}\PYZsh{} Feel free to use as many code cells as needed.}
\PY{n}{init} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{global\PYZus{}variables\PYZus{}initializer}\PY{p}{(}\PY{p}{)}
\PY{c+c1}{\PYZsh{} To save the model}
\PY{n}{saver} \PY{o}{=} \PY{n}{tf}\PY{o}{.}\PY{n}{train}\PY{o}{.}\PY{n}{Saver}\PY{p}{(}\PY{p}{)}
\PY{n}{save\PYZus{}file} \PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{./lenet\PYZus{}traffic.ckpt}\PY{l+s+s1}{\PYZsq{}}
\end{Verbatim}
\begin{Verbatim}[commandchars=\\\{\}]
{\color{incolor}In [{\color{incolor}206}]:} \PY{c+c1}{\PYZsh{} Running LeNet and Training on Training Set}
\PY{c+c1}{\PYZsh{} Measuring Accuracy on Validation Set}
\PY{k}{with} \PY{n}{tf}\PY{o}{.}\PY{n}{Session}\PY{p}{(}\PY{p}{)} \PY{k}{as} \PY{n}{sess}\PY{p}{:}
\PY{n}{sess}\PY{o}{.}\PY{n}{run}\PY{p}{(}\PY{n}{init}\PY{p}{)}
\PY{k}{for} \PY{n}{epoch} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{EPOCHS}\PY{p}{)}\PY{p}{:}
\PY{k}{for} \PY{n}{start\PYZus{}i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{,} \PY{n}{n\PYZus{}train}\PY{p}{,} \PY{n}{BATCH\PYZus{}SIZE}\PY{p}{)}\PY{p}{:}
\PY{n}{end\PYZus{}i} \PY{o}{=} \PY{n}{start\PYZus{}i} \PY{o}{+} \PY{n}{BATCH\PYZus{}SIZE}
\PY{n}{sess}\PY{o}{.}\PY{n}{run}\PY{p}{(}\PY{n}{optimizer}\PY{p}{,} \PY{n}{feed\PYZus{}dict}\PY{o}{=}\PY{p}{\PYZob{}}\PY{n}{x}\PY{p}{:} \PY{n}{X\PYZus{}train}\PY{p}{[}\PY{n}{start\PYZus{}i}\PY{p}{:}\PY{n}{end\PYZus{}i}\PY{p}{]}\PY{p}{,}
\PY{n}{y}\PY{p}{:} \PY{n}{y\PYZus{}train}\PY{p}{[}\PY{n}{start\PYZus{}i}\PY{p}{:}\PY{n}{end\PYZus{}i}\PY{p}{]} \PY{p}{,}
\PY{n}{keep\PYZus{}prob}\PY{p}{:} \PY{n}{DROPOUT}\PY{p}{\PYZcb{}}\PY{p}{)}
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\PY{n}{y}\PY{p}{:} \PY{n}{y\PYZus{}valid}\PY{p}{,}