afef_ahmed asked . 2021-07-15

How to read result of sim using Patternnet ?

I created neural network for binary classification , i have 2 classes and 9 features using an input matrix with a size of [9 981] and target matrix [1 981] . This is my code :

 rng(0);  
inputs = patientInputs;
targets = patientTargets;
[x,ps] = mapminmax(inputs);
t=targets; 
trainFcn = 'trainbr';  
% Create a Pattern Recognition Network
hiddenLayerSize =8;
net = patternnet(hiddenLayerSize,trainFcn);
net.divideFcn = 'dividerand';  % Divide data randomly
net.divideMode = 'sample';  % Divide up every sample
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
net.performFcn = 'mse';  
net.trainParam.max_fail=6;
% Choose Plot Functions
% For a list of all plot functions type: help nnplot
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
  'plotconfusion', 'plotroc'};
 % Train the Network
net= configure(net,x,t);
[net,tr] = train(net,x,t);
 y = net(x);
e = gsubtract(t,y);
performance = perform(net,t,y)
tind = vec2ind(t);
yind = vec2ind(y);
percentErrors = sum(tind ~= yind)/numel(tind);
 % Recalculate Training, Validation and Test Performance
trainTargets = t .* tr.trainMask{1};
valTargets = t .* tr.valMask{1};
testTargets = t .* tr.testMask{1};
trainPerformance = perform(net,trainTargets,y)
valPerformance = perform(net,valTargets,y)
testPerformance = perform(net,testTargets,y)
 % View the Network
view(net)

and when i tried to test the neural network with new data using

    ptst2 = mapminmax('apply',tst2,ps);
    bnewn = sim(net,ptst2);

I don't get the same values like the target i mean 0 or 1 however if i put test data with target 0 i have as a result of bnewn= 0.1835 and with data test having target 1 i got cnewn= 0.816. How can i read this results ? as i understand if it is >0.5 so target=1 else target=0

neural network , sim , patternnet , deep learning

Expert Answer

Prashant Kumar answered . 2025-03-29 17:07:39

To read and interpret the results of a neural network simulation using `patternnet` in MATLAB, follow these steps:

1. Train the Patternnet:
First, you need to train your `patternnet` with your input data and target data.

    
    % Example data
    x = rand(10, 100);  % 10 features, 100 samples
    t = rand(1, 100);   % 1 target, 100 samples
    
    % Create the network
    net = patternnet(10);  % 10 hidden neurons
    
    % Train the network
    net = train(net, x, t);

2. *Simulate the Network:
Use the trained network to simulate and obtain outputs for new input data.

   
    % New input data for simulation
    new_x = rand(10, 50);  % 10 features, 50 samples
    
    % Simulate the network
    y = net(new_x);

3. Interpret the Results:
The result `y` is the output of the network. Depending on your problem, you may need to interpret these results accordingly.

    matlab
    % Display the results
    disp(y);

4. Analyze the Performance:
Optionally, you can evaluate the performance of your network using appropriate metrics.

    
    % Performance evaluation
    performance = perform(net, t, y);
    disp(['Performance: ', num2str(performance)]);

This process involves training the `patternnet` with your data, simulating it with new inputs, and then interpreting the output results.

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