How to train feedforward network to solve XOR function
im new in matlab, please sorry if its stupid question. and sorry my english. trying to train feedforward network to solve XOR function 1 hidden layer with 2 neurons, other settings are default: TANSIG, Backprop, TRAINLM, LEARNGDM, MSE R2012b matlab version close all, clear all, clc, format compact p = [0 1 0 1 ; 0 0 1 1]; t = [0 1 1 0]; net = feedforwardnet(2,'trainlm'); net = train(net,p,t); a = net(p) ive tried this code, and tried 'nntool' and 'nnstart' too. its always seems like training algorithm splits 'p' set for 2 - training set, 1 - validation set, 1 - testing set as a result - network is training on partial data (2 pair of digits instead 4), and training process generates Validation done or Minimum gradient reached (1.00e-010) in very few iteration (1-10 iterations) and simulation shows that network untrained. Is my guess right (about splitting 'p' set)? how i can manually give validation data (input and output sets) to training algorithm? should i somehow expand 'p' and 't' sets, and then use divideblock? any other ideas?
Prashant Kumar answered .
2025-11-20
1.[ I N ] = size(x) % [ 2 4 ] [ O N ] = size(t) % [ 1 4 ] Neq = prod(size(t) % 4 = No. of training equations
2. For tthis small data set it doesn't make sense to use data division for validation stopping. So,
net.divideFcn = 'dividetrain'; % or equivalently, = ' ';
3. Since the No. of estimated weights for H hidden nodes is
%Nw = (I+1)*O = 3 for H=0 %Nw = (I+1)*H+(H+1)*O for H >0
the condition Neq >= Nw yields the following upper bound for H
Hub = (Neq-O)/(I+O+1) % 3/4
net = patternnet(2); % for classification
net.trainParam.goal = 0.01*var(t',1);
[ net tr y e ] = train(net,x,t);