shallow-training/report/report.lyx

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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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Andy Pack / 6420013
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May 2021
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Introduction
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Artificial neural networks have been the object of research and investigation
since the 1940s with
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McCulloch
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and
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Pitts
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or
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Throughout the century, the development of the single and multi-layer perceptro
ns (SLP/MLP) alongside the backpropagation algorithm
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advanced the study of artificial intelligence.
Throughout the 2010s, convolutional neural networks have proved critical
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
classify breast tumours as either benign or malignant.
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to evaluate how this affects performance.
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Investigations were carried out in
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Python
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using the
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TensorFlow
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package to construct, train and evaluate neural networks.
The networks were trained using a supervised learning curriculum of labelled
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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Section
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investigates the effect of varying the number of hidden nodes on test accuracy
along with the number of epochs that the MLPs are trained for.
Section
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builds on the previous experiment by using reasonable parameter values
to investigate performance when using an ensemble of models to classify
in conjunction.
The effect of varying the number of nodes and epochs throughout the ensemble
was considered in order to determine whether combining multiple models
could produce a better accuracy than those individually.
Section
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investigates the effect of altering how the networks learn by changing
the optimisation algorithm.
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using the same test apparatus of section
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.
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Hidden Nodes & Epochs (Exp 1)
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This section investigates the effect of varying the number of hidden nodes
in a single hidden layer of a multi-layer perceptron.
This is compared to the effect of varying
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Results
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Discussion
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Ensemble Classification (Exp 2)
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Results
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Optimiser Comparisons (Exp 3)
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Optimisers
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Stochastic Gradient Descent
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RMSprop
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Adam
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Results
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Discussion
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Overlapping 2D Gaussians (Exp 4)
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Conclusions
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Source Code
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