jsquared2.m

书籍“Regularization tools for training large feed-forward neural networks using Automatic Differentiat

function [AT2ones] =Jsquared2(mode,m,n,f,iw,rw, ...
	  w1,b1,Output_Layer2,Deriv_Layer2,w2,b2,Output_Layer3,Deriv_Layer3,P,T)
%Call: [AT2ones] =Jsquared2(mode,m,n,f,iw,rw, ...
  %w1,b1,Output_Layer2,Deriv_Layer2,w2,b2,Output_Layer3,Deriv_Layer3,P,T)
%
%This routine computes A(T) .^2 * ones(m,1) where A is the Jacobian
%corresponding to a FFANN with two hidden layers.
%The idea is that element k of AT2ones should contain the sum of the
%squared elements of the k:th column of the Jacobian
%
[Fst,q]=size(P);	% # nodes in Input layer (first layer)
[Snd,r]=size(w1);	% # nodes in 1'st hidden layer (second layer)
if Fst ~= r		% q is # samples presented to the inputs
   error('In Jsquared2: Fst .ne. r');
end
[Thrd,r]=size(w2);	% # nodes in output layer (third layer)
if Snd ~= r
   error('In Jsquared2: Snd .ne. r')
end
%[Fth,r]=size(w3);	% # nodes in output layer (forth layer)

Om=Fst*Snd+Snd;		% # weights in first hidden layer
Nm=Om+Snd*Thrd+Thrd;	% # total weights
%Mm=Nm+Thrd*Fth+Fth;	% # total weights
%lenf=max(size(f));
%if (q*Fth) ~= lenf
      %error('In Jsquared2: q*Fth .ne. lenf ');
    %else
      AT2ones=zeros(Nm,1);
%end
%
%Compute J(T) .^2 * ones(Nm,1)
%i.e. find the sum of the squared elements of all columns in J
%
%   time1=cputime;
      r1=Fst*Snd;
      r2=Om+Snd*Thrd;
      %r3=Nm+Thrd*Fth;
   %for i=1:q,
      %r4=(i-1)*Fth;
      %u(:,i)=f(r4+1:r4+Fth);
   %end
   %A3=Deriv_Layer4;
   %ta3=Deriv_Layer4 .^2;
   %w3bar=A3*Output_Layer3';	%Corresponding to w3bar
   %tw3bar=ta3 * (Output_Layer3 .^2)';
   %Af(Nm+1:r3)=w3bar(:);
   %AT2ones(Nm+1:r3)=tw3bar(:);
   %S=w3'*A3;
   %S=w3(1,:)'*Deriv_Layer4(1,:);
   %tS=(w3 .^2)' * ta3;
   %A2=Deriv_Layer3 .* S;
   %A2=Deriv_Layer3;
   %ta2=(Deriv_Layer3 .^2) .* tS;
   ta2=Deriv_Layer3 .^2;
   %w2bar=A2*Output_Layer2';	%Corresponding to w2bar
   tw2bar=ta2 * (Output_Layer2 .^2)';
   %Af(Om+1:r2)=w2bar(:);
   AT2ones(Om+1:r2)=tw2bar(:);
   S=w2(1,:)'*Deriv_Layer3(1,:);
   %tS=(w2 .^2)' * ta2;
   %A1=Deriv_Layer2 .* S;
   ta1=(Deriv_Layer2 .* S) .^2;
   %Af(r1+1:Om)=sum(A1')';	%Corresponding to b1
   AT2ones(r1+1:Om)=sum(ta1')';
   %w1bar=A1*P';			%Corresponding to w1bar
   tw1bar=ta1*(P .^2)';
   %Af(1:r1)=w1bar(:);
   AT2ones(1:r1)=tw1bar(:);
   for i=2:Thrd,
     S=w2(i,:)'*Deriv_Layer3(i,:);
     ta1=(Deriv_Layer2 .* S) .^2;
     tw1bar=ta1*(P .^2)';
     AT2ones(r1+1:Om)=AT2ones(r1+1:Om)+sum(ta1')';
     AT2ones(1:r1)=AT2ones(1:r1)+tw1bar(:);
   end
   %for i=2:Fth,
     %S=w3(i,:)'*Deriv_Layer4(i,:);
     %A2=Deriv_Layer3 .* S;
     %S=w2'*A2;
     %ta1=(Deriv_Layer2 .* S) .^2;
     %tw1bar=ta1*(P .^2)';
     %AT2ones(1:r1)=AT2ones(1:r1)+tw1bar(:);
     %AT2ones(r1+1:Om)=AT2ones(r1+1:Om)+sum(ta1')';
   %end
   %Af(Mm-Fth+1:Mm)=sum(A3')';	%Corresponding to b3
   %AT2ones(Mm-Fth+1:Mm)=sum(ta3')';
   %Af(Nm-Thrd+1:Nm)=sum(A2')';	%Corresponding to b2
   AT2ones(Nm-Thrd+1:Nm)=sum(ta2')';
end