Staffan asked . 2021-07-28

Using weights from OL in CL training; how should the weight vector

Using weights from OL in CL training; how should the weight vector(s)/cell matrices be formatted when used as input in train() ?

   close all
   clear all

% format long

   T = simplenar_dataset; 

   [I,N] = size(T);
   d = 5;
   FD = 1:d; 

   H = 10;

   % open net number one, input for closed net number one and closed net number two
   neto1 = narnet( FD, H );

   neto1.divideFcn = 'divideblock';
   [ Xo1, Xoi1, Aoi1, To1] = preparets( neto1, {}, {}, T );
   to = cell2mat( To1 ); 
  %  zto = zscore(to,1); 
    varto1 = mean(var(to',1));
  %  minmaxto = minmax([ to ; zto ]);

   rng( 'default' )
   [neto1,tro,Yo1,Eo1,Aof1,Xof1] = train( neto1, Xo1, To1, Xoi1, Aoi1 );
   [Yo1,Xof1,Aof] = neto1( Xo1, Xoi1, Aoi1 );
   Eo1 = gsubtract( To1, Yo1 );
   NMSEo1 = mse( Eo1 ) /varto1;
   yo1 = cell2mat( Yo1 );

   netc1 = closeloop(neto1);  
   EWo1=getwb(neto1);
   EWc1=getwb(netc1);

   isequal( EWo1, EWc1); % 1

   netc1.divideFcn = 'divideblock';
   [ Xc1, Xci1, Aci1, Tc1, EWc1 ] = preparets( netc1, {}, {}, T, EWo1 ); %  1.232667933023756e-08
   isequal( EWo1, cell2mat(EWc1)); % 1 if EWo1 is included in preparets, 0 if EWo1 is NOT included in preparets
   figure(1)
   plot(1:length(EWo1),EWo1,1:length(cell2mat(EWc1)),cell2mat(EWc1))

   isequal( Tc1, To1);
   tc = to;
   [netc1,troc1,Yc1,Ec1,Acf1,Xcf1] = train( netc1, Xc1, Tc1, Xci1, Aci1, EWc1); 
   % Here, in the training I would like to insert EWc1 to continute working weights from the
   % preparets which is nine lines up. However, when adding EWc1 as the last
   % input parameter I get the following  error: 

   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Error using nntraining.setup (line 17)
% Error weights EW{1,1 contains negative values.

% Error in network/train (line 292)
%    [net,rawData,tr,err] = nntraining.setup(net,net.trainFcn,X,Xi,Ai,T,EW,~isGPUArray);

%Error in question160516 (line 50)
% [netc1,troc1,Yc1,Ec1,Acf1,Xcf1] = train( netc1, Xc1, Tc1, Xci1, Aci1, EWc1);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

   % [netc1,troc1,Yc1,Ec1,Acf1,Xcf1] = train( netc1, Xc1, Tc1, Xci1, Aci1, EWc1); 
   EWc1=getwb(netc1);

   disp('Weights IW') % Here I try to show the content of each weight set
   o_iw=neto1.IW
   c_iw=netc1.IW

   disp('Weights LW')
   o_lw=neto1.LW
   c_lw=netc1.LW

   disp('Weights b')
   o_b=neto1.b
   c_b=netc1.b

   isequal( EWo1, EWc1); % 0
   figure(2)
   plot(1:length(EWo1),EWo1,1:length(EWc1),EWc1)

   [Yc1,Xcf1,Acf1] = netc1( Xc1, Xci1, Aci1 );
   Ec1 = gsubtract( Tc1, Yc1 );
   yc = cell2mat( Yc1 );
   NMSEc = mse(Ec1) /var(tc,1);

   [Yc1_2,Xcf1_2,Acf1_2] = netc1( Xc1, Xci1, Aci1 );
   Xc1_2 = cell(1,N);
   [Yc1_2,Xcf1_2,Acf1_2] = netc1( Xc1_2, Xcf1_2, Acf1_2 ); 
   yc1_2 = cell2mat(Yc1_2);

neural network , narnet , time series prediction