## Query regarding Artificial neural network

on 28 Oct 2012

### Greg Heath (view profile)

Hello could you please help me out with the answer of a question? 1. Say I am performing Face Recognition using PCA, now I have found out say 100 vectors i.e. eigenvectors of few classes. I have also set up the target matrix to train those vectors. Now, my question is when I am setting up the training ststem I have wriiten the matlab command as:- net=newff(final,target,9) where 9 is no. of layers of perceptrons, where final is the tarining samples. Now since I have 100 sample vectors , I may increase the no of vectors, so my question is should I increase the layers of perceptrons or how should I choose the 3rd argument in newff function. For training of 100 vectors is 9 layer of perceptrons ok? I shall be grateful to you if you kindly answer my question

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### Greg Heath (view profile)

on 30 Oct 2012

CORRECTION: You have one hidden layer with H = 9 hidden nodes. Do not use more hidden layers.

Design an I-H-O MLP for classification of O = c classes:

Use newpr (calls newff) or patternnet (calls feedforward net)

Input matrix x contains N I-dimensional column vectors

Target matrix t contains N O-dimensional unit column vectors with the row of the "1" indicating the class of the corresponding input vector.

Ntrn = 0.7*N % Default number of training examples

Ntrneq = Ntrn*O % Number of training equations

Nw = (I+1)*H +)H+1)*O % Number of unknown weights to estimate

H < < (Ntrneq-O)/(I+O+1) % Ntrneq > > Nw is desired

rng(0)

j=0

for h = 1:dH: Hmax

`   j=j+1`
`   for i = 1:Ntrials`
`       net = newpr(x,t,h);`
`       [net tr ] = train(net,x,t);`

% tr = tr % Important diagnostic info when needed

`        y = net(x);`
`       classes = vec2ind(y);`
`       fill this in`
```       PctErr(i,j) = ...
end
end```

etc

Hope this helps.

Thank you for formally accepting my answer.

Greg

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