Bouncing Neutrinos: Activity 20: Neural Networks

Monday, October 13, 2008

Activity 20: Neural Networks

Pattern recognition simply is making the computer recognize patterns we recognize visually. This begs the question: why not model pattern recognition the way our brain works? And this is what neural networks is about. Neural networks are composed of neurons arranged in a layer that is fully connected with the preceding layer. The layers are called the input, hidden and output layers respectively. The input layer is where the input features are fed and directed to the hidden layer which is again directed to the output layer. Neurons pass information using an "all-or-nothing" approach which is dependent on the strength of its connection to adjacent neurons.The tansig function is one of the most often used activation functions is pattern recognition problems.

For neural networks I used MATLAB instead of Scilab since MATLAB has an inbuilt Neural Networks toolbox while Scilab needs ANN. I used the parameters from Activity 18 for the 4 classes of images which are: Area, Height over Width, Average R, and Average G. Below is the code I used:

training = [5345 1.000000 0.758862 0.129754
6128 0.958763 0.756430 0.136960
5541 1.011494 0.814644 0.108821
1239 1.076923 0.602028 0.283912
1224 1.078947 0.629195 0.280192
1206 1.108108 0.611142 0.289349
439 0.444444 0.253336 0.508687
368 0.304348 0.248109 0.521104
372 0.444444 0.255933 0.518223
2080 1.000000 0.520683 0.374020
2064 1.040000 0.529217 0.382716
1977 1.083333 0.582884 0.345742 ]';
test = [ 5421 1.000000 0.795391 0.112691
5162 0.932584 0.826950 0.100648
5281 1.098765 0.788015 0.122597
1264 1.051282 0.633988 0.271862
1179 1.105263 0.618832 0.278147
1191 1.105263 0.615691 0.280110
306 0.409091 0.243423 0.517028
297 0.304348 0.245041 0.518146
304 0.239130 0.252019 0.514459
2000 0.980000 0.499452 0.372944
1956 1.020408 0.525111 0.379350
1906 1.062500 0.522208 0.392979 ]';

training_out = [0 0; 0 0; 0 0; 0 0; 0 1; 0 1; 0 1; 0 1; 1 1; 1 1; 1 1; 1 1]';

net=newff(minmax(training),[25,2],{'tansig','tansig'},'traingd');
net.trainParam.show = 50;
net.trainParam.lr = 0.01;
net.trainParam.lr_inc = 1.05;
net.trainParam.epochs = 1000;
net.trainParam.goal = 1e-2;
[net,tr]=train(net,training,training_out);
a = sim(net,training);
a = 1*(a>=0.5);
b = sim(net,test);
b = 1*(b>=0.5);
diff = abs(training_out - b);
diff = diff(1,:) + diff(2, :);
diff = 1*(diff>=1);

Again, 100% classification was observed.

Acknowledgments

Thanks to Jeric for helping me with the code. I understood this activity! i give myself 10/10 neutrinos!

posted by poy @ 2:43 AM

1 Comments:

At 3:45 PM, Lance said…

I'm really getting the hang of this, Paul! Haha