ACTIVITY 16 - Neural Networks

This activity is again related from the two previous activities, Activity 14 and 15. The purpose of this activity is to classify objects from their corresponding class using neural networks. The features of of the samples from the two classes used in Activity 15 was also used in this activity. The Clorets mint candy was tagged with the value of 0 while the Pillows chocolate snack was tagged with the value of 1.

The code below was made to implement the Artificial Neural Network algorithm.

clorets_train = fscanfMat('clorets_train.txt');
pillows_train = fscanfMat('pillows_train.txt');
clorets_test = fscanfMat('clorets_test.txt');
pillows_test = fscanfMat('pillows_test.txt');

cp_train = [clorets_train; pillows_train];
cp_train(:,1) = cp_train(:,1)/max(cp_train(:,1));
cp_train = cp_train';
cp_test = [clorets_test; pillows_test];
cp_test(:,1) = cp_test(:,1)/max(cp_test(:,1));
cp_test = cp_test';

rand('seed', 0);

network = [4, 4, 1];
groupings = [0 0 0 0 0 1 1 1 1 1];
learning_rate = [1, 0];
training_cycle = 1000;

training_weight = ann_FF_init(network);
weight = ann_FF_Std_online(cp_train, groupings, network, training_weight, learning_rate, training_cycle);
class = ann_FF_run(cp_test, network, weight);

** the source code was from Cole Fabro's work

The training parameters, learning rate and training cycle were tuned. It was observe that for a given training cycle, the recognition is more accurate with large learning rate. On the other hand, with the learning rate being constant, the recognition is also more accurate.

I will give myself a grade of 10/10 for this activity. Although the code was already given, I fully understand the effect of tuning the training parameters on the accuracy of the recognition. I thank Gilbert for helping me on this activity.