This repository contains code corresponding to the seminar paper:
D. Stutz. Introduction to Neural Networks. Seminar Report, Human Language Technology and Pattern Recognition Group, RWTH Aachen University, 2014.
Advisor: Pavel Golik
Update: The code can be adapted to allow mini-batch training as done in this fork.
The MNIST dataset provides a training set of 60,000 handwritten digits and a validation set of 10,000 handwritten digits. The images have size 28 x 28 pixels. Therefore, when using a two-layer perceptron, we need 28 x 28 = 784 input units and 10 output units (representing the 10 different digits).
The methods loadMNISTImages and loadMNISTLaels are used to load the MNIST dataset as it is stored in a special file format. The methods can be found online at http://ufldl.stanford.edu/wiki/index.php/Using_the_MNIST_Dataset.
The main method to train the two-layer perceptron is trainStochasticSquaredErrorTwoLayerPerceptron. The method applies stochastic training (or to be precise a stochastic variant of mini-batch training) using the sum-of-squared error function and the error backpropagation algorithm.
function [hiddenWeights, outputWeights, error] = trainStochasticSquaredErrorTwoLayerPerceptron(activationFunction, dActivationFunction, numberOfHiddenUnits, inputValues, targetValues, epochs, batchSize, learningRate)
% trainStochasticSquaredErrorTwoLayerPerceptron Creates a two-layer perceptron
% and trains it on the MNIST dataset.
%
% INPUT:
% activationFunction : Activation function used in both layers.
% dActivationFunction : Derivative of the activation
% function used in both layers.
% numberOfHiddenUnits : Number of hidden units.
% inputValues : Input values for training (784 x 60000)
% targetValues : Target values for training (1 x 60000)
% epochs : Number of epochs to train.
% batchSize : Plot error after batchSize images.
% learningRate : Learning rate to apply.
%
% OUTPUT:
% hiddenWeights : Weights of the hidden layer.
% outputWeights : Weights of the output layer.
%
The above method requires the activation function used for both the hidden and the output layer to be given as parameter. I used the logistic sigmoid activation function:
function y = logisticSigmoid(x)
% simpleLogisticSigmoid Logistic sigmoid activation function
%
% INPUT:
% x : Input vector.
%
% OUTPUT:
% y : Output vector where the logistic sigmoid was applied element by
% element.
%
In addition, the error backpropagation algorithm needs the derivative of the used activation function:
function y = dLogisticSigmoid(x)
% dLogisticSigmoid Derivative of the logistic sigmoid.
%
% INPUT:
% x : Input vector.
%
% OUTPUT:
% y : Output vector where the derivative of the logistic sigmoid was
% applied element by element.
%
The method applyStochasticSquaredErrorTwoLayerPerceptronMNIST uses both the training method seen above and the method validateTwoLayerPerceptron to evaluate the performance of the two-layer perceptron:
function [correctlyClassified, classificationErrors] = validateTwoLayerPerceptron(activationFunction, hiddenWeights, outputWeights, inputValues, labels)
% validateTwoLayerPerceptron Validate the twolayer perceptron using the
% validation set.
%
% INPUT:
% activationFunction : Activation function used in both layers.
% hiddenWeights : Weights of the hidden layer.
% outputWeights : Weights of the output layer.
% inputValues : Input values for training (784 x 10000).
% labels : Labels for validation (1 x 10000).
%
% OUTPUT:
% correctlyClassified : Number of correctly classified values.
% classificationErrors : Number of classification errors.
%
License for source code corresponding to:
D. Stutz. Introduction to Neural Networks. Seminar Report, Human Language Technology and Pattern Recognition Group, RWTH Aachen University, 2014.
Copyright (c) 2014-2018 David Stutz
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