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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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
for h = 1:dH: Hmax
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
Hope this helps.
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