A20 - Neural Networks

Neural network is a mathematical model that mimics brain function. It is made up of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons) in a similar manner shown below.

http://upload.wikimedia.org/wikipedia/en/thumb/1/1d/Neural_network_example.png/180px-Neural_network_example.png

In this activity, we use neural networks as a method to classify objects into classes depending on the extracted features. We again use pillows and kwek-kwet data set from activity19. The features used are ROI pixel area, length-to-width ratio, average red component (NCC) and average green component (NCC).

A code was already prepared by Jeric Tugaff and was only modified for this activity's purpose. The values used are as 4 input feature vectors (normalized between 0-1) both from 4 training objects for each class and 4 test objects for each class.

The neural network is trained using the training set. The code is expected to output values close to

[0 0 0 0 1 1 1 1]

meaning the first four test object will be classified as belonging to the pillows class and the second set of test objects will be classified under the kwek-kwek class. The output is as follows. From this table, 100% classification of the test objects is obviously obtained.

//code
chdir('C:\Documents and Settings\VIP\Desktop\ap186\A20');

training = fscanfMat('training.txt');
test = fscanfMat('test.txt');

//training
mntr = min(training, 'c');
tr2 = training - mtlb_repmat(mntr, 1, 8);
mxtr = max(tr2, 'c');
tr2 = tr2./mtlb_repmat(mxtr, 1, 8);

//test
mnts = min(test, 'c');
ts2 = test - mtlb_repmat(mnts, 1, 8);
mxts = max(ts2, 'c');
ts2 = ts2./mtlb_repmat(mxts, 1, 8);

tr_out = [0 0 0 0 1 1 1 1];
N = [4, 10, 1];
lp = [0.1, 0];
W = ann_FF_init(N);

T = 400;
W = ann_FF_Std_online(tr2,tr_out,N,W,lp,T);
//x is the training t is the output W is the initialized weights,
//N is the NN architecture, lp is the learning rate and T is the number of iterations

// full run
ann_FF_run(ts2,N,W) // classification output

//end code

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Thanks Jeric Tugaff for helping me understand how neural network works and for helping me with the program.

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Rating 7/10 since I implemented the program correctly but was very dependent on Jeric's tutorial and discussion. :)