DIGITS-CNN/report/report.lyx

#LyX 2.3 created this file. For more info see http://www.lyx.org/
\lyxformat 544
\begin_document
\begin_header
\save_transient_properties true
\origin unavailable
\textclass article
\begin_preamble
\def\changemargin#1#2{\list{}{\rightmargin#2\leftmargin#1}\item[]}
\let\endchangemargin=\endlist 
\pagenumbering{roman}

\usepackage{color}

\definecolor{commentgreen}{RGB}{0,94,11}
\end_preamble
\use_default_options true
\begin_modules
customHeadersFooters
minimalistic
todonotes
\end_modules
\maintain_unincluded_children false
\language british
\language_package default
\inputencoding auto
\fontencoding global
\font_roman "default" "default"
\font_sans "default" "default"
\font_typewriter "default" "default"
\font_math "auto" "auto"
\font_default_family default
\use_non_tex_fonts false
\font_sc false
\font_osf false
\font_sf_scale 100 100
\font_tt_scale 100 100
\use_microtype true
\use_dash_ligatures true
\graphics default
\default_output_format default
\output_sync 0
\bibtex_command biber
\index_command default
\paperfontsize 11
\spacing onehalf
\use_hyperref true
\pdf_title "Convolutional Neural Networks with DIGITS"
\pdf_author "Andy Pack"
\pdf_subject "EEEM063 Image Processing & Deep Learning"
\pdf_keywords "EEEM063"
\pdf_bookmarks true
\pdf_bookmarksnumbered false
\pdf_bookmarksopen false
\pdf_bookmarksopenlevel 1
\pdf_breaklinks false
\pdf_pdfborder true
\pdf_colorlinks false
\pdf_backref false
\pdf_pdfusetitle true
\papersize default
\use_geometry true
\use_package amsmath 1
\use_package amssymb 1
\use_package cancel 1
\use_package esint 1
\use_package mathdots 1
\use_package mathtools 1
\use_package mhchem 1
\use_package stackrel 1
\use_package stmaryrd 1
\use_package undertilde 1
\cite_engine biblatex
\cite_engine_type authoryear
\biblio_style plain
\biblio_options urldate=long
\biblatex_bibstyle ieee
\biblatex_citestyle ieee
\use_bibtopic false
\use_indices false
\paperorientation portrait
\suppress_date true
\justification true
\use_refstyle 1
\use_minted 0
\index Index
\shortcut idx
\color #008000
\end_index
\leftmargin 2cm
\topmargin 2cm
\rightmargin 2cm
\bottommargin 2cm
\secnumdepth 3
\tocdepth 3
\paragraph_separation skip
\defskip medskip
\is_math_indent 0
\math_numbering_side default
\quotes_style british
\dynamic_quotes 0
\papercolumns 1
\papersides 1
\paperpagestyle fancy
\bullet 1 0 9 -1
\bullet 2 0 24 -1
\tracking_changes false
\output_changes false
\html_math_output 0
\html_css_as_file 0
\html_be_strict false
\end_header

\begin_body

\begin_layout Title

\size giant
Convolutional Neural Networks with DIGITS
\end_layout

\begin_layout Author
Andy Pack
\end_layout

\begin_layout Standard
\begin_inset VSpace 15pheight%
\end_inset


\end_layout

\begin_layout Standard
\align center
\begin_inset Graphics
	filename surrey.png
	lyxscale 15
	width 40col%

\end_inset


\end_layout

\begin_layout Standard
\begin_inset VSpace vfill
\end_inset


\end_layout

\begin_layout Standard
\noindent
\align center
EEEM063
\begin_inset Newline newline
\end_inset

May 2021
\size large

\begin_inset Newline newline
\end_inset

Department of Electrical and Electronic Engineering
\begin_inset Newline newline
\end_inset

Faculty of Engineering and Physical Sciences
\begin_inset Newline newline
\end_inset

University of Surrey
\end_layout

\begin_layout Standard
\begin_inset Newpage newpage
\end_inset


\end_layout

\begin_layout Abstract
abstract
\end_layout

\begin_layout Standard
\begin_inset CommandInset toc
LatexCommand tableofcontents

\end_inset


\end_layout

\begin_layout List of TODOs

\end_layout

\begin_layout Standard
\begin_inset Newpage newpage
\end_inset


\end_layout

\begin_layout Standard
\begin_inset FloatList figure

\end_inset


\end_layout

\begin_layout Standard
\begin_inset FloatList table

\end_inset


\end_layout

\begin_layout Standard
\begin_inset CommandInset toc
LatexCommand lstlistoflistings

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Newpage newpage
\end_inset


\end_layout

\begin_layout Right Footer
Andy Pack / 6420013
\end_layout

\begin_layout Left Footer
May 2021
\end_layout

\begin_layout Left Header
EEEM063 Coursework
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
pagenumbering{arabic} 
\end_layout

\begin_layout Plain Layout


\backslash
setcounter{page}{1}
\end_layout

\end_inset


\end_layout

\begin_layout Section
Introduction
\end_layout

\begin_layout Standard
Although much of the theory for convolutional neural networks (CNNs) was
 developed throughout the 20th century, their importance to the field of
 computer vision was not widely appreciated until the early 2010s.
 
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
More context
\end_layout

\end_inset


\end_layout

\begin_layout Standard
Although CNNs can appear opaque when attempting to understand how decisions
 are made, they are not black boxes and there are many ways to affect a
 model's performance.
 This work presents investigations into how a CNN's performance is affected
 by the subject dataset, the architecture of the network and the parameters
 used when training.
 Section 
\begin_inset CommandInset ref
LatexCommand ref
reference "sec:Investigations-Scope"
plural "false"
caps "false"
noprefix "false"

\end_inset

 outlines the scope of the investigations made herein, describing the motivation
 for the variations and expectations as to how this would affect performance.
 The results for these investigations are presented in section 
\begin_inset CommandInset ref
LatexCommand ref
reference "sec:Results"
plural "false"
caps "false"
noprefix "false"

\end_inset

 with interpretations made in the following section.
 Section 
\begin_inset CommandInset ref
LatexCommand ref
reference "sec:Conclusions"
plural "false"
caps "false"
noprefix "false"

\end_inset

 summarises and concludes the work.
\end_layout

\begin_layout Section
Investigations Scope
\begin_inset CommandInset label
LatexCommand label
name "sec:Investigations-Scope"

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Dataset
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Dataset Processing
\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Mean shift/whitening/augmentation
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Meta-Parameters
\end_layout

\begin_layout Standard
\begin_inset Flex TODO Note (inline)
status open

\begin_layout Plain Layout
Epochs/learning rate/momentum?
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Network Architectures
\end_layout

\begin_layout Subsubsection
Convolutional Layers
\end_layout

\begin_layout Subsubsection
Fully-Connected Layers
\end_layout

\begin_layout Standard
Following the convolutional stages there are three dense or fully-connected
 layers which provide two key features in image classification.
 The first is flattening the 2D cross-section of the preceding convolutional
 layers into a 1D representation for propagation to a final one-hot vector
 output.
 The second is as a traditional multi-layer perceptron classifier, taking
 the high-level visual insights of the later convolutional layers and reasoning
 these into a final classification.
 When treated as an MLP, these can instead be considered as 2 hidden layers
 and a single output layer.
 The reason for designating the last layer separately is the level to which
 it is fixed when varying the classifier as a whole.
 The number of neurons in this layer remains equal to the number of classes
 in the dataset in order to form a one-hot vector output when the network
 makes a classification.
\end_layout

\begin_layout Subsubsection
Non-Linearity
\end_layout

\begin_layout Standard
The inclusion of non-linear layers throughout AlexNet is critical to it's
 ability to learn complex insights into a dataset.
 Convolution as a mathematical operation can be proven to be associative
\begin_inset Flex TODO Note (Margin)
status open

\begin_layout Plain Layout
Fubini's theorem
\end_layout

\end_inset

 in a similar fashion to multiplication.
 This means that consecutive convolutions can be collapsed into a single
 operation, for example multiple filters can be merged into a single compound
 operation for less expensive application to an image.
 
\end_layout

\begin_layout Section
Results
\begin_inset CommandInset label
LatexCommand label
name "sec:Results"

\end_inset


\end_layout

\begin_layout Section
Discussion
\begin_inset CommandInset label
LatexCommand label
name "sec:Discussion"

\end_inset


\end_layout

\begin_layout Section
Conclusions
\begin_inset CommandInset label
LatexCommand label
name "sec:Conclusions"

\end_inset


\end_layout

\begin_layout Standard
\begin_inset Newpage newpage
\end_inset


\end_layout

\begin_layout Standard
\begin_inset CommandInset label
LatexCommand label
name "sec:bibliography"

\end_inset


\begin_inset CommandInset bibtex
LatexCommand bibtex
btprint "btPrintCited"
bibfiles "references"
options "bibtotoc"

\end_inset


\begin_inset Newpage pagebreak
\end_inset


\end_layout

\begin_layout Section
\start_of_appendix
Source Code
\begin_inset CommandInset label
LatexCommand label
name "sec:Code"

\end_inset


\end_layout

\end_body
\end_document