I don't Know why my neural network doesn't give good results

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Sarah Mahmood
Sarah Mahmood 2013 年 11 月 14 日
編集済み: Greg Heath 2018 年 8 月 25 日
I tried to build NN for spoken word classification I followed many approaches discussed here in th group, yet the results terrible I got only ~65% correct classification, I don't what wrong I'm so desperate.
I appreciate any help or notice on my code or the approach I followed
load yesClass2;
load noClass2;
yes2=1*ones(1,199);
no2=zeros(1,208);
InData=[yesClass2 ;noClass2];
InData=InData';
TarData=[yes2 no2];
xtrn=InData;
ttrn=TarData;
[ I N ] = size( xtrn ) % [ 5 407 ]
[ O N ] = size( ttrn ) % [ 1 407 ]
MSEtrn00 = mean(var(ttrn',1))
MSEgoal = MSEtrn00/100
MinGrad = MSEtrn00/300
%rng(0)
net4 = patternnet(58,'trainlm');
net4.divideFcn = 'dividetrain';
net4.trainParam.goal = MSEgoal;
net4.trainParam.min_grad = MinGrad;
[net4 tr ] = train(net4,xtrn,ttrn);
bestepoch = tr.best_epoch;
R2 = 1 - tr.perf(bestepoch)/MSEtrn00;
save net4 net4
  1. I chose No. of hidden nodes 58 based on max. R2 achieved
  2. max. R2 = 0.9
  3. I attached confusion matrix for complete data set used for training and for divided data set into 70% training, 30% validation and 30% testing
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Greg Heath
Greg Heath 2018 年 8 月 25 日
編集済み: Greg Heath 2018 年 8 月 25 日
Totally confusing post
=====================
Came back later and struggled through. I understand now.
Next time you post code please explain exactly what you are doing and why.
In particular 58 hidden nodes is overfitting when the data is divided.
Unfortunately, the MATLAB confusion matrices are not very easy to understand.
I have posted a better code for understanding (might be in the NEWSGROUP).
Greg

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Greg Heath
Greg Heath 2013 年 11 月 29 日
This is a classic case of overtraining an overfit net:
DIVIDETRAIN:
Nw = (5+1)*58+(58+1)*1 = 348 + 59 = 407 % Unknown weights
Ntrneq = 407*1 = 407 % Equations
The no overfitting condition Ntrneq >> Nw is readily violated
DIVIDERAND:
Ntrneq = 407 -2*round(0.15*407) = 285 < 407
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KAE
KAE 2018 年 8 月 24 日
編集済み: KAE 2018 年 8 月 24 日
Here are Greg's calculations with extra comments to help other learners,
% Number of rows: number of features. Number of columns: number of samples.
[ I N ] = size( xtrn ) % Size of network inputs
[ O N ] = size( ttrn ) % Size of network targets
% H is number of neurons in hidden layer, here 58
fractionTrain = 0.15; % Fraction of data used as training examples.
% 0.15 is the default, which is assumed here
fractionValid = 0.15; % Fraction of data used as validation examples
% 0.15 is the default
Ntrn = N - round(fractionTrain*N + fractionValid*N); % Number of training examples
Ntrneq = Ntrn*O; % Number of training equations
Nw = (I+1)*H + (H+1)*O; % Number of unknown weights
% Since we want Ntrnreq>>Nw, we could require that Nw<Ntrneq/10
% But it's not, so we are overfitting
% Must either get more data (training examples), or
% simplify our model (smaller H)
The plot here is helpful for understanding overfitting.

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