Asked by Ampi
on 28 Oct 2012

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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Answer by Greg Heath
on 30 Oct 2012

Accepted answer

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

Opportunities for recent engineering grads.

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