I am using the Iris Data Set to train my NN using Back Propagation. The code is attached.
p = [
5.1,3.5,1.4,0.2; %iris data set
4.9,3.0,1.4,0.2;
4.7,3.2,1.3,0.2;
4.6,3.1,1.5,0.2;
5.0,3.6,1.4,0.2;
5.4,3.9,1.7,0.4;
4.6,3.4,1.4,0.3;
5.0,3.4,1.5,0.2;
4.4,2.9,1.4,0.2;
4.9,3.1,1.5,0.1;
5.4,3.7,1.5,0.2;
4.8,3.4,1.6,0.2;
4.8,3.0,1.4,0.1;
4.3,3.0,1.1,0.1;
5.8,4.0,1.2,0.2;
5.7,4.4,1.5,0.4;
5.4,3.9,1.3,0.4;
5.1,3.5,1.4,0.3;
5.7,3.8,1.7,0.3;
5.1,3.8,1.5,0.3;
5.4,3.4,1.7,0.2;
5.1,3.7,1.5,0.4;
4.6,3.6,1.0,0.2;
5.1,3.3,1.7,0.5;
4.8,3.4,1.9,0.2;
5.0,3.0,1.6,0.2;
5.0,3.4,1.6,0.4;
5.2,3.5,1.5,0.2;
5.2,3.4,1.4,0.2;
4.7,3.2,1.6,0.2;
4.8,3.1,1.6,0.2;
5.4,3.4,1.5,0.4;
5.2,4.1,1.5,0.1;
5.5,4.2,1.4,0.2;
4.9,3.1,1.5,0.1;
5.0,3.2,1.2,0.2;
5.5,3.5,1.3,0.2;
4.9,3.1,1.5,0.1;
4.4,3.0,1.3,0.2;
5.1,3.4,1.5,0.2;
5.0,3.5,1.3,0.3;
4.5,2.3,1.3,0.3;
4.4,3.2,1.3,0.2;
5.0,3.5,1.6,0.6;
5.1,3.8,1.9,0.4;
4.8,3.0,1.4,0.3;
5.1,3.8,1.6,0.2;
4.6,3.2,1.4,0.2;
5.3,3.7,1.5,0.2;
5.0,3.3,1.4,0.2;
7.0,3.2,4.7,1.4;
6.4,3.2,4.5,1.5;
6.9,3.1,4.9,1.5;
5.5,2.3,4.0,1.3;
6.5,2.8,4.6,1.5;
5.7,2.8,4.5,1.3;
6.3,3.3,4.7,1.6;
4.9,2.4,3.3,1.0;
6.6,2.9,4.6,1.3;
5.2,2.7,3.9,1.4;
5.0,2.0,3.5,1.0;
5.9,3.0,4.2,1.5;
6.0,2.2,4.0,1.0;
6.1,2.9,4.7,1.4;
5.6,2.9,3.6,1.3;
6.7,3.1,4.4,1.4;
5.6,3.0,4.5,1.5;
5.8,2.7,4.1,1.0;
6.2,2.2,4.5,1.5;
5.6,2.5,3.9,1.1;
5.9,3.2,4.8,1.8;
6.1,2.8,4.0,1.3;
6.3,2.5,4.9,1.5;
6.1,2.8,4.7,1.2;
6.4,2.9,4.3,1.3;
6.6,3.0,4.4,1.4;
6.8,2.8,4.8,1.4;
6.7,3.0,5.0,1.7;
6.0,2.9,4.5,1.5;
5.7,2.6,3.5,1.0;
5.5,2.4,3.8,1.1;
5.5,2.4,3.7,1.0;
5.8,2.7,3.9,1.2;
6.0,2.7,5.1,1.6;
5.4,3.0,4.5,1.5;
6.0,3.4,4.5,1.6;
6.7,3.1,4.7,1.5;
6.3,2.3,4.4,1.3;
5.6,3.0,4.1,1.3;
5.5,2.5,4.0,1.3;
5.5,2.6,4.4,1.2;
6.1,3.0,4.6,1.4;
5.8,2.6,4.0,1.2;
5.0,2.3,3.3,1.0;
5.6,2.7,4.2,1.3;
5.7,3.0,4.2,1.2;
5.7,2.9,4.2,1.3;
6.2,2.9,4.3,1.3;
5.1,2.5,3.0,1.1;
5.7,2.8,4.1,1.3;
6.3,3.3,6.0,2.5;
5.8,2.7,5.1,1.9;
7.1,3.0,5.9,2.1;
6.3,2.9,5.6,1.8;
6.5,3.0,5.8,2.2;
7.6,3.0,6.6,2.1;
4.9,2.5,4.5,1.7;
7.3,2.9,6.3,1.8;
6.7,2.5,5.8,1.8;
7.2,3.6,6.1,2.5;
6.5,3.2,5.1,2.0;
6.4,2.7,5.3,1.9;
6.8,3.0,5.5,2.1;
5.7,2.5,5.0,2.0;
5.8,2.8,5.1,2.4;
6.4,3.2,5.3,2.3;
6.5,3.0,5.5,1.8;
7.7,3.8,6.7,2.2;
7.7,2.6,6.9,2.3;
6.0,2.2,5.0,1.5;
6.9,3.2,5.7,2.3;
5.6,2.8,4.9,2.0;
7.7,2.8,6.7,2.0;
6.3,2.7,4.9,1.8;
6.7,3.3,5.7,2.1;
7.2,3.2,6.0,1.8;
6.2,2.8,4.8,1.8;
6.1,3.0,4.9,1.8;
6.4,2.8,5.6,2.1;
7.2,3.0,5.8,1.6;
7.4,2.8,6.1,1.9;
7.9,3.8,6.4,2.0;
6.4,2.8,5.6,2.2;
6.3,2.8,5.1,1.5;
6.1,2.6,5.6,1.4;
7.7,3.0,6.1,2.3;
6.3,3.4,5.6,2.4;
6.4,3.1,5.5,1.8;
6.0,3.0,4.8,1.8;
6.9,3.1,5.4,2.1;
6.7,3.1,5.6,2.4;
6.9,3.1,5.1,2.3;
5.8,2.7,5.1,1.9;
6.8,3.2,5.9,2.3;
6.7,3.3,5.7,2.5;
6.7,3.0,5.2,2.3;
6.3,2.5,5.0,1.9;
6.5,3.0,5.2,2.0;
6.2,3.4,5.4,2.3;
5.9,3.0,5.1,1.8;
]';
t = [
0; %assign 0 to output neuron for Iris-setosa
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0;
0.5; %assign 0.5 to output neuron for Iris-versicolor
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
0.5;
1; %assign 1 to output neuron for Iris-virginica
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
1;
]';
net = feedforwardnet(3,'traingd'); %3 hidden layers and training algorithm
net = configure(net,p,t);
net.layers{2}.transferFcn = 'logsig'; %sigmoid function in output layer
net.layers{1}.transferFcn = 'logsig'; %sigmiod fucntion in hidden layer
net.performFcn = 'mse';
net = init(net);
net.trainParam.epochs = 10000;
net.trainParam.lr = 0.7; %learning rate
net.trainParam.goal = 0.01; %mse
net = train(net,p,t);
view(net);
The problem is that I am not getting the desired output for the first class (for which the output should be close to zero). When I input a vector from the first class to the trained net, the output is close to 0.5 (but it should be close to zero).
This is the output for the first vector of the first class:
output = net([5.1,3.5,1.4,0.2]')
output = 0.5003
This output should be close to zero (because I have assigned 0 to first class), but it is coming out to be 0.5. This is the case for all the inputs of first class. For the second and third class, the outputs are fine i.e. close to 0.5 for class 2 and close to 1.0 for class 3.
Can you please run this code and tell me what I am doing wrong?
(I think it might be issue of the bias input because all the outputs for class 1 are being offset by 0.5.)
Regards.
