need help "backpropagation algorithm with more than one hidden layer"

i have implemented backpropagation network for one hidden layer, but now my guide told me to use more than one hidden layer. can any one tell me: # what is the method for distribution of neurons in hidden layers(>1)? # following initialization weight and bias matrices are right for hidden layers=2? v=randn(n,x); w=randn(x,m); wo=randn(1,m); vo=randn(1,x); v1=randn(x,(p-x)); %2nd hidden layer vo1=randn(1,(p-x)); %2nd hidden layer vin=v; win=w; woin=wo; voin=vo; v1in=v1; %2nd hidden layer vo1in=vo1; %2nd hidden layer 3) whether the following code is correct for 2 hidden layers? p-hidden layer neurons x=round(p/2)%2 is no. of hidden layers %%%First hidden layer zin=zeros(trn_dat,x); z = zeros(trn_dat,x); din = zeros(trn_dat,x); d = zeros(trn_dat,x); delv = zeros(n,x); delvo = zeros(1,x); %%%Second hidden layer zin1=zeros(x,p-x); z1=zeros(x,p-x); din1 = zeros(x,p-x); d1 = zeros(x,p-x); delv1 = zeros(x,p-x); delvo1 = zeros(1,p-x); %%%Output Layer yin = zeros(trn_dat,m); y = zeros(trn_dat,m); dk = zeros(trn_dat,m); delw = zeros(p-x,m); delwo = zeros(1,m); er=0; itr=1; max_itr=1000; while er==0 disp('epoch no is'); disp(itr); for T=1:trn_dat %total no. of training records %%feed forward for j=1:x zin(T,j)=0; for i=1:n zin(T,j)=zin(T,j)+(dt(T,i)*v(i,j)); end zin(T,j)=zin(T,j)+vo(j); z(T,j)=(2/(1+exp(-zin(T,j))))-1; end for j=(p-x) zin1(j)=0; for i=1:x zin1(j)=zin1(j)+(z(T,i)*v1(i,j)); end zin1(j)=zin1(j)+vo1(j); z1(j)=(2/(1+exp(-zin1(j))))-1; end for k=1:m yin(T,k)=0; for j=1:(p-x) yin(T,k)=yin(T,k)+(z1(j)*w(j,k)); end yin(T,k)=yin(T,k)+w(k); y(T,k)=(2/(1+exp(-yin(T,k))))-1; end %%back propagation of error for k=1:m dk(T,k)=0; dk(T,k)=((tar(T,k)-y(T,k))*((1/2)*(1+y(T,k))*(1-y(T,k)))); for j=1:(p-x) delw(j,k)=(alpha*dk(T,k)*z1(j)); end delwo(k)=(alpha*dk(T,k)); end for j=1:(p-x) din1(j)=0; for k=1:m din1(j)=din1(j)+((dk(T,k)*w(j,k))); end d1(j)=0; d1(j)=(din1(j)*((1/2)*(1+z1(j))*(1-z1(j))));

                        for i=1:x
                            delv1(i,j)=(alpha*d1(j)*z(T,j));
                        end
                        delvo1(j)=(alpha*d1(j));
                        for i=1:n
                            delv(i,j)=alpha*d(T,j)*dt(T,i);
                        end
                        delvo(j)=alpha*d(T,j);
                    end
                    %%updation of weights and biases
                    for k=1:m
                        for j=1:(p-x)
                            w(j,k)=w(j,k)+delw(j,k);
                        end
                        wo(k)=wo(k)+delwo(k);
                    end
                    for j=1:(p-x)
                        for i=1:x
                            v1(i,j)=v1(i,j)+delv1(i,j);
                        end
                        vo1(j)=vo1(j)+delvo1(j);
                    end
                    for j=1:x
                        for i=1:n
                            v(i,j)=v(i,j)+delv(i,j);
                        end
                        vo(j)=vo(j)+delvo(j);
                    end
                end
                disp('value of y at this iteration:');
                disp(y);
                %    error=0;
                e=tt-y;
                error=sqrt(e.^2);
                if (max(max(error))<0.05)
                    er=1;
                else
                    er=0;
                end
                itr=itr+1;
                if(itr>max_itr)
                    break;
                end
              end

thank you

Punam

[Oleg Komarov]

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