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\pdf_title "Training Neural Networks With Backpropagation"
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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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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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' model of the artificial neuron
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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.
 The architecture and parameters were varied before exploring how in order
 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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Deep Learning Toolbox
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.
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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.
 Two additional algorithms to the previously used are considered and compared
 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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Discussion
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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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