What work space values do i need to save separately to test Classification
What work space values do i need to save separately to test Classification of a number of voice emotion recognition neural networks and compare a new input against several to give a result?
TrainingHappyInput = NNHappyTrainingInput;
TragetHappy= ones(1,1536);
% set to Happy = 2
TragetHappy=TragetHappy*2;
net = newff([min(TrainingHappyInput) max(TrainingHappyInput)],[14 1],{'tansig' 'purelin'},'traingd');
net.trainParam.epochs = 2900; %Maximum number of epochs to train
net.trainParam.goal = 0.01; %Performance goal
net.trainParam.lr = 0.01; %Learning rate
net.trainParam.min_grad=1e-10; %Minimum performance gradient
net.trainParam.show = 25; %Epochs between displays
net.trainParam.time = inf; %Maximum time to train in seconds HappyTestset=TrainingHappyInput(400:700); NetOutputHappyTestData = sim(net,HappyTestset);
subplot(2,1,1),
plot(TrainingHappyInput(400:700),TragetHappy(400:700),HappyTestset,NetOutputHappyTestData,'o')
title('Accuracy of classification');
HappyDiffTraining = TragetHappy (400:700)- NetOutputHappyTestData;
subplot(2,1,2), plot(HappyDiffTraining);
title('Difference Between Trained/Targets'); HappyClassifiedTrained = mean(NetOutputHappyTestData); if HappyClassifiedTrained > 1.8078
disp('Emotion detected is HAPPY...!');
else
disp('Not Classified as Happy');
end
This is the code I currently have for a Happy emotion and will have a similar network for sad etc. Having never studied Matlab or signal processing to finish this project I need to test a voice sample against the neural networks and output which emotion has been detected. Happy is Target is set to 2 as in the code, sad will be 3, neutral 1 etc. I don’t know what variables i need to collect from each workspace and how to compare a test sample to all the networks and have a decision produced. I think it will be similar to the if statement at the end of the code above but can’t work what I need to do.
ANSWER
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There are many fundamental problems.
0. You have spelled Target wrong 1. NEWFF with special cases NEWFIT (for curve"FITTING" and
regression) and NEWPR (for "P"attern "R"ecognition) are obsolete.
Their replacements are FEEDFORWARDNET, FITNET and PATTERNNET,
respectively. 2. This leads to the obvious question: "WHAT VERSION OF MATLAB ARE
YOU USING???" 3. I assume you have a recent version and should be using PATTERNNET.
So, see the documentation with simple examples obtained from the
commands help patternnetand doc patternnet.
4. Additional examples and explanation can be obtained from searches in both the NEWSGROUP and ANSWERS.
For example
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