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Topic: Improving ANN results
Replies: 16   Last Post: Nov 13, 2013 9:10 AM

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chaudhry

Posts: 26
Registered: 10/13/13
Re: Improving ANN results
Posted: Oct 23, 2013 1:20 AM
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"chaudhry " <bilal_zafar9@yahoo.com> wrote in message <l3omd4$bhb$1@newscl01ah.mathworks.com>...
> "Greg Heath" <heath@alumni.brown.edu> wrote in message <l3kj60$mlt$1@newscl01ah.mathworks.com>...
> > "Greg Heath" <heath@alumni.brown.edu> wrote in message <l3khqr$1fe$1@newscl01ah.mathworks.com>...
> > > "chaudhry " <bilal_zafar9@yahoo.com> wrote in message <l3ebuu$evs$1@newscl01ah.mathworks.com>...
> > >

> > > > How to improve ANN results by reducing error through hidden layer size, through MSE, or by using while loop?
> > >
> > > Your data is not a good learning example. (Small size, constant x(1,:), weak relationship between input and target )
> > >
> > > 1. Practice on MATLAB data (e.g., simplefit_dataset)

> >
> > close all, clear all, clc
> > format short
> >
> > x = [31 9333 2000;31 9500 1500;31 9700 2300;31 9700 2320;...
> > 31 9120 2230;31 9830 2420;31 9300 2900;31 9400 2500]'
> > t = [35000;23000;3443;2343;1244;9483;4638;4739]'
> > xnew = [31 9333 2000]'
> >
> > % [ x, t ] = simplefit_dataset; % Better learning example
> >
> > [ I N ] = size( x ) % [ 3 8 ]
> > [ O N ] = size( t ) % [ 1 8 ]
> >
> > %Standardization?
> > varx = var( x') % 1e5 * [ 0 0.585 1.63 ] %Huge
> > vart = var( t ) % 1.47e8 % Ditto
> >
> > % Delete x(1,:) and standardize
> >
> > x = x(2:3,:); %Omit for simplefit_dataset
> > zx = zscore(x',1)';
> > zt = zscore(t',1)';
> > MSE00 = var(t',1) % = 1 Reference MSE
> > Ntst = round(0.15*N) % = 1 default
> > Ntrials = max(10,30/Ntst) % 30
> >
> > % Use default No. of hidden nodes (10)
> >
> > net = fitnet;
> >
> > rng(0)
> > for i=1:Ntrials
> > net = configure(net,x,t);
> > [net tr ] = train(net,x,t);
> > R2trn(i,1) = 1 - tr.best_perf/MSE00;
> > R2val(i,1) = 1 - tr.best_vperf/MSE00;
> > R2tst(i,1) = 1 - tr.best_tperf/MSE00;
> > end
> > R2s = [ R2trn R2val R2tst ]
> >
> > minR2s = min(R2s) % -13.2021 -17.1237 -22.9422
> > medR2s = median(R2s) % 0.7096 0.4177 0.1100
> > meanR2s = mean(R2s) % -0.8757 -1.2760 -1.8358
> > stdR2s = std(R2s) % 3.4060 3.9508 4.8567
> > maxR2s = max(R2s) % 1.0000 1.0000 0.9965
> > sortR2s = sort(R2s)
> >
> > % sortR2s = -13.2021 -17.1237 -22.9422
> > % -10.4006 -9.8592 -8.5426
> > % -4.5224 -6.3693 -6.8485
> > % -4.1019 -5.0681 -6.5369
> > % -3.7170 -3.6865 -5.7350
> > % -2.0340 -2.4595 -5.0384
> > % -1.7259 -2.4565 -4.0096
> > % -1.3995 -1.6079 -3.6787
> > % -1.2526 -1.6025 -0.5167
> > % -0.2069 -1.0937 -0.3695
> > % -0.1213 -0.6480 -0.2804
> > % 0.1603 -0.2390 -0.2782
> > % 0.4618 0.1275 -0.2124
> > % 0.6146 0.1944 -0.1174
> > % 0.6782 0.2760 0.0623
> > % 0.7410 0.5594 0.1577
> > % 0.9138 0.7007 0.2807
> > % 0.9301 0.7012 0.3786
> > % 0.9488 0.7098 0.4315
> > % 0.9736 0.7654 0.4789
> > % 0.9917 0.9362 0.4834
> > % 0.9999 0.9819 0.6334
> > % 1.0000 0.9890 0.7886
> > % 1.0000 0.9957 0.8014
> > % 1.0000 0.9982 0.8439
> > % 1.0000 0.9996 0.9090
> > % 1.0000 0.9996 0.9091
> > % 1.0000 0.9997 0.9253
> > % 1.0000 0.9998 0.9510
> > % 1.0000 1.0000 0.9965
> >
> > % Note that only 2 of 30 designs have R2tst >= 0.95 !!!
> >
> > % In contrast, for the simplefit_data set (x(1,:) NOT deleted)
> > %
> > % Ntrials = 10
> > % R2s = 1.0000 1.0000 1.0000
> > % 1.0000 1.0000 1.0000
> > % 1.0000 1.0000 1.0000
> > % 1.0000 1.0000 1.0000
> > % 1.0000 1.0000 1.0000
> > % 1.0000 1.0000 1.0000
> > % 1.0000 1.0000 1.0000
> > % 1.0000 1.0000 1.0000
> > % 1.0000 0.9997 1.0000
> > % 1.0000 1.0000 0.9999
> >
> > Now try minimizing the number of hidden nodes for the simplefit example.
> >
> > Hope this helps.
> >
> > Greg

>
>
> HI GREG THNX ALOT ....I HAVE FEW Questions regarding the above code MY QUESTIONS MAY SEEMS STUPID BECAUSE IAM BEGINNER . I HAVE database of around 80 cross 30 parameters.
>
> 1.SIR y u have chose Ntrials parameter for forloop for training purpose.....can v use for example
> such that errors=targets-outputs
> while errors~=0 ..........
> train(net,x).................
> some thing like that.....or
> while mse~=0
> train(net,x)
> is it right or wrong i dont knw.......if wrong kindly tell me the reason
>
>
> 2. why sir u havent considered weights ,,biases..epochs...etc.. and learning rate value....trainlm is independent of lr BUT Y then how he make system learn...from example
>
> 3.sir y v standardize
> 4.kindly explain these lines
>
> zx = zscore(x',1)';

> > zt = zscore(t',1)';
> > MSE00 = var(t',1) % = 1 Reference MSE
> > Ntst = round(0.15*N) % = 1 default
> > Ntrials = max(10,30/Ntst) % 30

>
> R2trn(i,1) = 1 - tr.best_perf/MSE00;

> > R2val(i,1) = 1 - tr.best_vperf/MSE00;
> > R2tst(i,1) = 1 - tr.best_tperf/MSE00;

>
>
> why v have found medians..means....etc
>


heelllo sir greg

two more questions regarding the above question


1. > > % Note that only 2 of 30 designs have R2tst >= 0.95 !!! this thing you have told me after the results. sir did this mean that our data is not appropiate because 2 out 30 designs were fine. so i will train it again with other technique.
2. what is the problem with default data division which is generated through advanced script.
3. sit you have used FOR LOOP for Ntrials1:10.sir why dont u have use it with while loop such

while %certain best performance or least mse is achieved ....and it will strat loop from ntrial=1 then 2 then 3 then 4 etc and it will stop when certain mse or error is achieved
>



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