Dear All;
i am using the standard NN code in matlab , i notice that without using data normalization , i get better results , Does this code normalization :
clc; clear; close all; [x1,TXT,RAW]=xlsread('Model.xlsx','VslVsg'); [t1,TXT2,RAW2]=xlsread('Model.xlsx','OUT'); x=x1'; t=t1'; size(x) size(t)
net = newpr(x,t,20); % view(net) net=init(net); [net,tr] = train(net,x,t); nntraintool
plotperform(tr)
%% Testing the Classifier % The trained neural network can now be tested with the testing samples % This will give us a sense of how well the network will do when applied % to data from the real world. % % The network outputs will be in the range 0 to 1, so we can use vec2ind % function to get the class indices as the position of the highest element % in each output vector.
testX = x(:,tr.testInd); testT = t(:,tr.testInd);
testY = net(testX); testIndices = vec2ind(testY)
%% % One measure of how well the neural network has fit the data is the % confusion plot. Here the confusion matrix is plotted across all samples. % % The confusion matrix shows the percentages of correct and incorrect % classifications. Correct classifications are the green squares on the % matrices diagonal. Incorrect classifications form the red squares. % % If the network has learned to classify properly, the percentages in the % red squares should be very small, indicating few misclassifications. % % If this is not the case then further training, or training a network % with more hidden neurons, would be advisable.
plotconfusion(testT,testY)
%% % Here are the overall percentages of correct and incorrect classification.
[c,cm,ind,per] = confusion(testT,testY) errors = gsubtract(testT,testY); fprintf('Percentage Correct Classification : %f%%\n', 100*(1-c)); fprintf('Percentage Incorrect Classification : %f%%\n', 100*c);
%% % Another measure of how well the neural network has fit data is the % receiver operating characteristic plot. This shows how the false % positive and true positive rates relate as the thresholding of outputs % is varied from 0 to 1. % % The farther left and up the line is, the fewer false positives need to % be accepted in order to get a high true positive rate. The best % classifiers will have a line going from the bottom left corner, to the % top left corner, to the top right corner, or close to that.
plotroc(testT,testY)
