adaptlr.m

新的神经网络算法源程序

function [t,d,w,c,b,SSE] = adaptlr(x,y,t,d,w,c,b, SSE)
%Adaptlr: Adaptive learning rate
%
%	[t,d,w,c,b,SSE] = adaptlr(x,y,t,d,w,c,b, SSE)
%
%	By Qinghua Zhang. April 10, 1992.

%First call. updatewn returns hessien and gradient only for the first call
lr = 0;
[t_tmp,d_tmp,w_tmp,c_tmp,b_tmp, hess_t, hess_d, dt, dd] ...
		 			= updatewn(x,y,t,d,w,c,b,lr);
if length(b_tmp) > 0 		% not bad matrix condition
 g = wavenet(x,t_tmp,d_tmp,w_tmp,c_tmp,b_tmp);
 E = y-g;
 SSE_current = sum(E .* E); %Sum of Squared Errors

 if SSE_current <= SSE % Good step
   t = t_tmp; d = d_tmp; w = w_tmp; c = c_tmp; b = b_tmp;
   SSE = SSE_current;
   return;
 end
end

% A bad step was made, then go on!!
%==================================
lr = 0.1; % start value for Learning Rate
inc_dec = 0; % set indicator for increase or decrease  lr

while 1
  [t_tmp,d_tmp,w_tmp,c_tmp,b_tmp] ...
   = updatewn(x,y,t,d,w,c,b,lr, hess_t, hess_d, dt, dd); %send back hessien 
  							 % and gradient
  if length(b_tmp) > 0 		% not bad matrix condition
    g = wavenet(x,t_tmp,d_tmp,w_tmp,c_tmp,b_tmp);
    E = y-g;
    SSE_current = sum(E .* E); %Sum of Squared Errors
    if SSE_current <= SSE    %good, stop or decrease lr
      if inc_dec == 1 % lr increased for last step, so stop
        break;
      else 	      % should decrease lr, hoping to do better
        SSE = SSE_current; % next step should compare with this SSE
        lr = 0.5 * lr;
        inc_dec = -1;
        %Prepare to come back to this step
        t_last=t_tmp; d_last=d_tmp; w_last=w_tmp; c_last=c_tmp; b_last=b_tmp;
        SSE_last=SSE;
      end
    else		     %bad, so increase lr go back to the last step
      if inc_dec == -1  % lr decreased for last step, go back to the last step
        t_tmp=t_last; d_tmp=d_last; w_tmp=w_last; c_tmp=c_last; b_tmp=b_last;
        SSE = SSE_last;
        break;
      else	        % lr increased for last step, still needs to increase

        if (lr > 10) & (SSE_current < 1.01*SSE_current) % RISK of infinite loop
          disp(' Slightly increasing error.'); break
        end

        lr = 2.0 * lr;
        inc_dec = 1;
      end
    end
  else				% bad matrix condition, should increase lr
    lr = 2.0 * lr;
    inc_dec = 1;
  end
end

%Return the good result
%======================
t=t_tmp; d=d_tmp; w=w_tmp; c=c_tmp; b=b_tmp;
SSE = SSE_current;