2021-03-02 23:06:09 +00:00
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\size giant
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2021-03-29 19:17:14 +01:00
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Training Neural Networks with Backpropagation
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2021-03-02 23:06:09 +00:00
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\end_layout
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\begin_layout Author
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Andy Pack
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EEEM005
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\begin_inset Newline newline
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\end_inset
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May 2021
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\size large
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Department of Electrical and Electronic Engineering
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Faculty of Engineering and Physical Sciences
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University of Surrey
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2021-03-28 18:37:32 +01:00
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\begin_layout Section*
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Executive Summary
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Summary here
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\begin_layout Abstract
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abstract
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2021-03-29 19:17:14 +01:00
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Andy Pack / 6420013
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May 2021
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pagenumbering{arabic}
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\begin_layout Section
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Introduction
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\end_layout
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2021-04-06 21:15:26 +01:00
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\begin_layout Standard
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Artificial neural networks have been the object of research and investigation
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since the 1940s with
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\noun on
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McCulloch
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and
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Pitts
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' model of the artificial neuron
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or
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Threshold Logic Unit
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.
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Throughout the century, the development of the single and multi-layer perceptro
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ns (SLP/MLP) alongside the backpropagation algorithm
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\begin_inset CommandInset citation
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advanced the study of artificial intelligence.
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Throughout the 2010s, convolutional neural networks have proved critical
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in the field of computer vision and image recognition
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\begin_layout Standard
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This work investigates the ability of a shallow multi-layer perceptron to
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classify breast tumours as either benign or malignant.
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The architecture and parameters were varied before exploring how in order
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to evaluate how this affects performance.
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Investigations were carried out in
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\noun on
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Python
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\noun default
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using the
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TensorFlow
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\noun default
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package to construct, train and evaluate neural networks.
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The networks were trained using a supervised learning curriculum of labelled
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data taken from a standard
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MatLab
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dataset
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from the
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Deep Learning Toolbox
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\end_layout
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\begin_layout Standard
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Section
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\begin_inset CommandInset ref
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LatexCommand ref
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reference "sec:exp1"
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plural "false"
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caps "false"
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noprefix "false"
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\end_inset
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investigates the effect of varying the number of hidden nodes on test accuracy
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along with the number of epochs that the MLPs are trained for.
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Section
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\begin_inset CommandInset ref
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LatexCommand ref
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reference "sec:exp2"
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plural "false"
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caps "false"
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noprefix "false"
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\end_inset
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builds on the previous experiment by using reasonable parameter values
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to investigate performance when using an ensemble of models to classify
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in conjunction.
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The effect of varying the number of nodes and epochs throughout the ensemble
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was considered in order to determine whether combining multiple models
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could produce a better accuracy than those individually.
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Section
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\begin_inset CommandInset ref
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LatexCommand ref
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reference "sec:exp3"
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plural "false"
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caps "false"
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noprefix "false"
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\end_inset
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investigates the effect of altering how the networks learn by changing
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the optimisation algorithm.
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Two additional algorithms to the previously used are considered and compared
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using the same test apparatus of section
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\begin_inset CommandInset ref
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LatexCommand ref
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reference "sec:exp2"
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plural "false"
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caps "false"
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noprefix "false"
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\end_inset
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.
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\end_layout
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2021-03-28 18:37:32 +01:00
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\begin_layout Section
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Hidden Nodes & Epochs (Exp 1)
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2021-04-06 21:15:26 +01:00
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\begin_inset CommandInset label
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LatexCommand label
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name "sec:exp1"
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\end_inset
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\end_layout
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\begin_layout Standard
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This section investigates the effect of varying the number of hidden nodes
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in a single hidden layer of a multi-layer perceptron.
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This is compared to the effect of varying
|
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\end_layout
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\begin_layout Subsection
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Results
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\end_layout
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\begin_layout Subsection
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Discussion
|
2021-03-28 18:37:32 +01:00
|
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\end_layout
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\begin_layout Section
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Ensemble Classification (Exp 2)
|
2021-04-06 21:15:26 +01:00
|
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|
\begin_inset CommandInset label
|
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LatexCommand label
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name "sec:exp2"
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\end_inset
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\end_layout
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\begin_layout Subsection
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Results
|
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\end_layout
|
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\begin_layout Subsection
|
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Discussion
|
2021-03-28 18:37:32 +01:00
|
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|
\end_layout
|
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|
\begin_layout Section
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Optimiser Comparisons (Exp 3)
|
2021-04-06 21:15:26 +01:00
|
|
|
\begin_inset CommandInset label
|
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|
|
|
LatexCommand label
|
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|
|
name "sec:exp3"
|
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|
\end_inset
|
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\end_layout
|
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\begin_layout Subsection
|
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|
Optimisers
|
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|
\end_layout
|
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|
\begin_layout Subsubsection
|
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|
|
Stochastic Gradient Descent
|
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|
\end_layout
|
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|
\begin_layout Subsubsection
|
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|
|
RMSprop
|
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|
\end_layout
|
|
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|
\begin_layout Subsubsection
|
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|
|
Adam
|
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|
|
|
\end_layout
|
|
|
|
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|
\begin_layout Subsection
|
|
|
|
|
Results
|
|
|
|
|
\end_layout
|
|
|
|
|
|
|
|
|
|
\begin_layout Subsection
|
|
|
|
|
Discussion
|
2021-03-28 18:37:32 +01:00
|
|
|
\end_layout
|
|
|
|
|
|
|
|
|
|
\begin_layout Section
|
|
|
|
|
Overlapping 2D Gaussians (Exp 4)
|
|
|
|
|
\end_layout
|
|
|
|
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|
|
|
|
|
\begin_layout Section
|
|
|
|
|
Conclusions
|
|
|
|
|
\end_layout
|
|
|
|
|
|
2021-03-02 23:06:09 +00:00
|
|
|
\begin_layout Standard
|
|
|
|
|
\begin_inset Newpage newpage
|
|
|
|
|
\end_inset
|
|
|
|
|
|
|
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|
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|
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|
|
|
\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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
\end_layout
|
|
|
|
|
|
|
|
|
|
\begin_layout Section
|
|
|
|
|
\start_of_appendix
|
|
|
|
|
Source Code
|
|
|
|
|
\end_layout
|
|
|
|
|
|
2021-03-29 19:17:14 +01:00
|
|
|
\begin_layout Standard
|
|
|
|
|
\begin_inset CommandInset include
|
|
|
|
|
LatexCommand lstinputlisting
|
|
|
|
|
filename "../nncw.py"
|
|
|
|
|
lstparams "caption={Formatted Jupyter notebook containing experiment code},label={notebook-code}"
|
|
|
|
|
|
|
|
|
|
\end_inset
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
\end_layout
|
|
|
|
|
|
2021-03-02 23:06:09 +00:00
|
|
|
\end_body
|
|
|
|
|
\end_document
|
