Arranging the input and target matrix in Artificial neural network classification problem.

Hello, I am using matlab for 6 months, so considering myself as a beginner, please help me in this. My goal is to classify a feature matrix consisting 16 features 80 images of leaf(1st 40 affected, next 40 normal) each. so the matrix size is 16x80. the name of that matrix is feature_trans. As I understand it will be the input matrix. Now I made the target matrix. It is a 2x80 matrix, where from index 1-40, row 1 = 0, row 2=1. and from index 40-80, row 1 = 1, row 2 = 0 (As I mentioned 1st 40 affected, next 40 normal ) and the name of that matrix is Target1. I have studied one example from mathworks and just replaced cancerInputs with feature_trans and cancerTargets with Target1. the original code i have used is from http://in.mathworks.com/help/nnet/gs/classify-patterns-with-a-neural-network.html

My code:

if true
  % code
end
% Solve a Pattern Recognition Problem with a Neural Network
% Script generated by NPRTOOL
%                                
% This script assumes these variables are defined:
%
%   cancerInputs - input data.
%   cancerTargets - target data.
inputs = feature_trans;
targets = Target1;
% Create a Pattern Recognition Network
hiddenLayerSize = 10;
net = patternnet(hiddenLayerSize);
% Set up Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
% Train the Network
[net,tr] = train(net,inputs,targets);
% Test the Network
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs)
% View the Network
view(net)
% Plots
% Uncomment these lines to enable various plots.
figure, plotperform(tr)
figure, plottrainstate(tr)
figure, plotconfusion(targets,outputs)
figure, ploterrhist(errors)

********************************************

But the accuracy is 52%. But i have tested the same data with SVM CLASSIFIER and i got 96% accuracy. I couldnot understand where the loophole is. Please help me in this matter.

Answer 1

[Greg Heath]

1. Please reformat your post so that the code will run when cut and pasted.

2. Assuming every thing else is OK, you need to have a good combination of H(No. of hidden nodes) and rng (RNG state that determines the initial random weights).

3. Search using the NEWSGROUP and ANSWERS using

 greg patternnet

to see how I use a double loop approach to find the best combination.

4. However, in this case I think the answer is with I = 16, O = 2, N = 80 and H = 10 you have more unknown weights

 Nw = (I+1)*H +(H+1)*O = 192

than training equations

 Ntrneq = Ntrn*O = 0.7*N*O  = 112.

Possible contributions to a remedy:

   1. Increase N
   2. Reduce H 
   3. Multiple random initial weight designs
   4. Reduce I. The input rankings obtained from the linear models 
      STEPWISEFIT or PLSREGRESS might be useful.
   5. Use MSEREG and/or TRAINBR

Hope this helps.

Thank you for formally accepting my answer

Greg

P.S. Let us know what happens.

[debasmita bhoumik]

Thank you sir, I will let you know the outcome. I am not able to write the code segments as others do. while i press the {}code button, it comes

if true
  % code
end

where to paste my code then, so than format will be correct?

[debasmita bhoumik]

Thanks a lot, for your detailed answer. The entire problem is solved by 2. Reduce H and 5. Use TRAINBR. now my training accruary is 100% and testing is 97%. Thank you so very much.