MATLAB: Bad results of the neural network _ newsgroup

MATLAB: Bad results of the neural network _ newsgroup

Deep Learning Toolboxneural network

Hi Dr Greg i tried to reply to your message in the NEWSGROUP in this link several times but but it dosen’t post , i don’t know why so i decide to post it here as a question because i couldn’t have any solution .
This is the message that i wanted to post :
I appology for that DR Greg this will not be repeated
> x = patientInputs;
> t = patientTargets;
> inputs = mapstd(x);
> targets = mapstd(t);
>> I don't remember suggesting mapstd. Data is automatically normalized
and unnormalized by the training function. The only reason to use mapstd
is to detect and delete or modify outliers.
I asked you because of the format of my inputs as you can see in this link Actually I tried to use MAPSTD but it dosen’t improve so i applied the logarithme to the inputs that have a very high value numerically compared to other inputs but also it doesn’t work .
> N=981
>>Why does this differ from the 1012 of previous posts?
It was at first 981 but i wanted to increase the number of N to see if this will improve the peformance and as i see that there is no big progress i returned to 981
> Ntrn = N-2*round(0.15*N) %

> Ntrneq = Ntrn*O %
>>If you are not going to use an ending semicolon, then print the answer
after the percent sign. For example
[ I N ] = size(x) % [ 9 981]
[ O N ] = size(t) % [ 2 981 ]
Ntrn = N-2*round(0.15*N) % 687
Ntrneq = Ntrn*O % 1374
> %For a robust design desire Ntrneq >> Nw or
> H=10
or H = 10 ???
>>That doesn't make any sense
This was just a typing error
> Hub = -1+ceil( (Ntrneq-O) / (I+O+1)) % Hub =117
>>117?? Your arithmetic sucks.
> Ntrials = 10
> rng(0)
> j=0
> for h =round([Hub])
>>Where in the world did you get that from? Hub = 114 !
> j = j+1
> h = h %12
>> h = 114! Where did you get 12 from??
Actually i got this value after running the code and it was from the given results
> trueclasses = vec2ind(t);
> for i = 1:Ntrials
> net = configure(net,x,t);
> [ net tr outputs errors ] = train(net,x,t);
> assignedclasses = vec2ind(outputs);
> classerr = assignedclasses~=trueclasses;
> Nerr(i,j) = sum(classerr);
> % FrErr = Fraction of Errors (Nerr/N)
> [FrErr(i,j),CM,IND,ROC] = confusion(t,outputs);
> FN(i,j) = mean(ROC(:,1)); % Fraction of False Negatives
> TN(i,j) = mean(ROC(:,2)) ; % Fraction of True Negatives
> TP(i,j) = mean(ROC(:,3)); % Fraction of True Positives
> NMSE = mse(errors)/mean(var(t',1))
> end
> end
> PctErr=100*Nerr/N
> NMSE = mse(errors)/mean(var(t',1))
>>This makes no sense: You have Ntrials designs but only take
the last one instead of the best one
but i tried to add NMSE = mse(errors)/mean(var(t’,1)) and it was a fault from me to put it after the loop.
My real problem is how can i use this result to improve the performance of my neural network So as i understand that for Not overfitting Ntrneq >= Nw so i can have idea about the limit of H because Nw = 12*H+2 and i should decrease H to have NMSE <= 0.01 * MSE00 . Ami right ?
Again i apology for this bad Etiquette but i hope that you understand me because i have no idea about neural network and Matlab and i have no time for my project this is why .
Thank you Dr greg for you patience .

Best Answer

  • 1. You did not define net = patternnet
    2. Even though it is a classifier, just try to minimize H subject to the constraint
    mse(e) <= 0.01*mean(var(t',1)) % e=t-y
    NMSE <= 0.01
    Rsquare >= 0.99
    Hope this helps
    Thank you for formally accepting my answer
    3. If you overfit, validation stopping will kick in
    Good Luck
    Thank you for formally accepting my answer
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