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() ? I am trying to attach the weights obtained in OL in the CL training. I can see that the amount of data contained in the weight sets; .IW, .LW and .B are altered when going from open loop to closed loop....still, the weight vector obtained from getwb() have the same amount of data for both in OL and CL. Any ideas how to format the weight vector (in the code below the weight vector is designated EWc1) before inserting this to train()? Is there any way that preparets() (or a similar function) can handle this? Code and error message: 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); If you would like to run the code without getting the error, just remove EWc1 from the end of [netc1,troc1,Yc1,Ec1,Acf1,Xcf1] = train( netc1, Xc1, Tc1, Xci1, Aci1, EWc1);
Kshitij Singh answered .
2025-11-20
clc
% Code and error message:
close all
clear all
% format long
T = simplenar_dataset;
[ I, N ] = size(T) % [ 1 100 ]
d = 5
GEH2= ' WHY 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)) % 0.062747
% minmaxto = minmax([ to ; zto ]);
rng( 'default' )
% [neto1,tro,Yo1,Eo1,Aof1,Xof1] = train( neto1, Xo1, To1, Xoi1, Aoi1 );
GEH3 = ' ERROR1: SWITCH Aof1 and Xof1'
[neto1,tro,Yo1,Eo1,Xof1,Aof1] = train( neto1, Xo1, To1, Xoi1, Aoi1);
%[Yo1,Xof1,Aof] = neto1( Xo1, Xoi1, Aoi1 );
GEH4 = 'ERROR: Aof1 not Aof'
%Eo1 = gsubtract( To1, Yo1 );
GEH5 = ' COMMENT ABOVE 2 REDUNDANT STATEMENTS'
NMSEo1 = mse( Eo1 ) /varto1 %1.6546e-09
GEH6 = ' ALWAYS MAKE SURE NMSEo1 IS ADEQUATE BEFORE CL'
yo1 = cell2mat( Yo1 );
netc1 = closeloop(neto1);
EWo1=getwb(neto1);
EWc1=getwb(netc1);
isequal( EWo1, EWc1) % 1
GEH7 = [ 'INCORRECT NOTATION: EW IS RESERVED FOR MSE' ...
' ERROR WEIGHTS. USE WBo1 AND WBc1 FOR WEIGHT '...
' BIAS VECTORS ' ]
%netc1.divideFcn = 'divideblock';
GEH8 = 'ABOVE ASSIGNMENT IS UNNECESSARY'
[ Xc1, Xci1, Aci1, Tc1, EWc1 ] = preparets( netc1, {}, {}, T, EWo1 ); % 1.232667933023756e-08
GEH9 = 'ERROR: SEE GEH0'
GEH10 = 'WHAT IN THE WORLD IS 1.232667933023756e-08 ???'
GEH11 = 'DELETE ABOVE 3 STATEMENTS' isequal( Tc1, To1); tc = to; [netc1,troc1,Yc1,Ec1,Acf1,Xcf1] = train( netc1, Xc1, Tc1, Xci1, Aci1, EWc1); GEH12 = 'ERRORS: 1: SWITCH Acf1 AND Xcf1 2: REMOVE EWc1' GEH13 = 'I"LL STOP HERE'