565 lines
9.6 KiB
Plaintext
565 lines
9.6 KiB
Plaintext
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Training Neural Networks with Backpropagation
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Andy Pack
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EEEM005
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May 2021
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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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Executive Summary
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Summary here
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\begin_layout Abstract
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abstract
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Andy Pack / 6420013
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May 2021
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\begin_layout Section
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Introduction
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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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\emph on
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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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key "Rumelhart1986"
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literal "false"
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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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.
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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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\end_layout
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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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\noun on
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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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\noun on
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MatLab
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\noun default
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dataset
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key "matlab-dataset"
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literal "false"
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from the
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\noun on
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Deep Learning Toolbox
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\noun default
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.
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Section
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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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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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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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\begin_layout Section
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Hidden Nodes & Epochs (Exp 1)
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\begin_inset CommandInset 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
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\end_layout
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\begin_layout Section
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Ensemble Classification (Exp 2)
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\begin_inset CommandInset 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
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\end_layout
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\begin_layout Section
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Optimiser Comparisons (Exp 3)
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\begin_inset CommandInset label
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name "sec:exp3"
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\end_inset
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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
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Results
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\end_layout
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\begin_layout Subsection
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Discussion
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\end_layout
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\begin_layout Section
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Overlapping 2D Gaussians (Exp 4)
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\end_layout
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\begin_layout Section
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Conclusions
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Source Code
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