MATLAB: How to improve the accuracy of confusion matrix of neural network

Deep Learning Toolboxneural network

Hi, i used nprtool to create neural network for classification and i have dataset with input matrix 9*981 and target matrix 2*981. This is my code :
 x = inputpatient';
 t = targetpatient';
 % Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'trainscg';  % Scaled conjugate gradient backpropagation.
 % Create a Pattern Recognition Network
hiddenLayerSize = 10;
net = patternnet(hiddenLayerSize);
 % Setup 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,x,t);
 % Test the Network
y = net(x);
e = gsubtract(t,y);
performance = perform(net,t,y)
tind = vec2ind(t);
yind = vec2ind(y);
percentErrors = sum(tind ~= yind)/numel(tind);
 % View the Network
view(net)
 % Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, ploterrhist(e)
%figure, plotconfusion(t,y)
%figure, plotroc(t,y)
After training my net i got this confusion matrix
I want to improve my net because as you can see from my confusion matrix the accuracy is 65.9 % . Please i need help can anyone give me advice ?

Best Answer

1. Search both the NEWSGROUP and ANSWERS using
 greg patternnet
and
 greg patternnet tutorial
2. Many of the posts
   a. Use as many defaults as possible
   b. Search for the smallest successful number of hidden nodes and 
      corresponding random initial weights using a double loop 
      approach:
        i) Outer loop over number of hidden nodes
       ii)  Inner loop over random initial weights.
Hope this helps.
Thank you for formally accepting my answer
Greg
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