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Dear reader,

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);

Regards

Staffan

Greg Heath
on 18 May 2016

Edited: Greg Heath
on 18 May 2016

% Using weights from OL in CL training; how should the % weight vector(s)/cell matrices be formatted when used % as input in train() ? % 2 views (last 30 days) % Asked by Staffan 17MAY2016 % % 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?

GEH0 =[ ' YOU HAVE CONFUSED NETWORK WEIGHT BIAS '... ' VECTORS, WB, FROM GETWB WITH ERROR ' ... ' WEIGHTS, EW, OF LENGTH N THAT ARE CHOSEN ' ... ' BY THE PROGRAMMER TO WEIGHT EACH TERM ' .. ' IN MEAN SQUARE ERROR ' ]

GEH1 = 'I REMOVED SOME ENDING SEMICOLONS BELOW TO CHECK RESULTS'

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 ???'

% 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))

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'

HOPE THIS HELPS

Greg

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