matlab - Trouble with backpropogation in a vectorized implementation of a simple neural network -


i have been going through ufldl tutorials.in vectorized implementation of simple neural net, tutorials suggest 1 way go through entire training set instead of iterative approach. in propogation part, mean replacing:

 gradw1 = zeros(size(w1));  gradw2 = zeros(size(w2));   i=1:m,   delta3 = -(y(:,i) - h(:,i)) .* fprime(z3(:,i));    delta2 = w2'*delta3(:,i) .* fprime(z2(:,i));    gradw2 = gradw2 + delta3*a2(:,i)';   gradw1 = gradw1 + delta2*a1(:,i)';  end;

with

delta3 = -(y - h) .* fprime(z3) delta2 = w2'*delta3().*fprime(z2)  gradw2 = delta3*a2' gradw1 = delta2*a1'  //apply weight correction gradients //are computed

please visit this page information notation , algorithm.

however implementation yielded abnormally large values inside gradw1 , gradw2. seems result of me not updating weights process each training input(tested on earlier working implementation). right this? reading tutorials seems there way make work, can't come works.

backpropogation has 2 ways of implementation: batch , online training algorithm. described online training algorithm. found , tried implement batch training algorithm sometime has side effect described. in case can idea split learning samples smaller chunks , learn on them.


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