An error occuered before neural network training

Hi

I want to classify time series data by unsupervised learning

So,I'm making recurrent self organized neural network by Shallow Neural Network.

This error occuered

error: +

The dimensions of the matrix must match.(Original txt:行列の次元は一致しなければなりません。)

error: trainbu>train_network (line 196)

net.IW{i,j} = net.IW{i,j} + dw;

error: trainbu (line 52)

[out1,out2] = train_network(varargin{2:end});

error: network/train (line 353)

[net,tr] = feval(net.trainFcn,'apply',net,tr,data,calcMode.options,hints,net.trainParam);

error: SOM_test (line 79)

[net2,tr] = train(net2,Xs,{},Xi,Ai);

Program I wrote is this

[X, Y]= simpleseries_dataset;
net2.userdata='fujimura_kengo';

dimension1 = 5;
dimension2 = 5;
net2= layrecnet(1:2,10);
net2.numLayers=2;
net2.name=('Den-O');
net2.trainFcn='trainbu';

net2.numInputs=1;

net2.layers{1}.initFcn='initwb';
net2.layers{1}.netInputFcn='netsum';

%2層目の設定
net2.layers{2}.dimensions=[5 5];
net2.layers{2}.distanceFcn='linkdist'   ;
net2.layers{2}.initFcn= 'initwb'   ;
net2.layers{2}.netInputFcn= 'netsum'  ;
net2.layers{2}.topologyFcn= 'hextop'   ;
net2.layers{2}.transferFcn= 'compet'  ;
net2.layers{2}.netInputParam='netprod';
net2.layers{2}.transferParam='elliotsig';
net2.layers{2}.distanceParam='dist';


net2.inputConnect=[1 ; 0 ];
net2.layerConnect=[1  0; 1 0 ];
net2.outputConnect=[0 1 ];
net2.biasConnect=[0;0];
net2.divideFcn=''


view(net2);

[net2,tr] = train(net2,Xs,{},Xi,Ai);

I want fix this error.But I can't ditect cause of error

If someone know how to fix,please teach me how to fix

Answer 1

[Hiroyuki Hishida]

Hi Kengo,

I tried reproduce your phenomemon, but it was failed since 'Xs', 'Xi' and 'Ai' are not defined.

Could you add more information?

Best

Hiroyuki