Weights don't initialize.

 I created the following network:
  P = dataH;
  T = dataXsm;
  net=network;
  net.numInputs = 1;
  net.numLayers = 3;
  net.biasConnect(1) = 1;
  net.biasConnect(2) = 1;
  net.biasConnect(3) = 1;
  net.inputConnect = [1; 0; 0];
  net.layerConnect = [0 0 0; 1 0 0; 0 1 0];
  net.outputConnect = [0 0 1];
  net.inputs{1}.size = 2;
  net.layers{1}.size = 2;
  net.layers{1}.transferFcn = 'hardlim';
  net.layers{1}.initFcn = 'initnw';
  net.layers{2}.size = 10;
  net.layers{2}.transferFcn = 'hardlim';
  net.layers{2}.initFcn = 'initnw';
  net.layers{3}.size = 10;
  net.layers{3}.initFcn = 'initnw';
  net.layers{3}.transferFcn = 'hardlim';
  net.initFcn = 'initlay';
  net.IW{1,1}, net.IW{2,1}, 
  net.LW{3,2}
  net.b{1}, net.b{3}
  net.trainFcn = 'trainc';
  net.performFcn = 'sse';
  net.adaptFcn = 'trains';
  net.trainParam.goal=0.01;
  net.trainParam.epochs=100;
  net.trainParam.passes = 1;
  net = init(net);
  a = sim(net,P), e = T-a
  net=train(net,P,T);
  net.adaptParam.passes = 100;
  [net,a,e] = adapt(net,P,T); e
  twts = net.IW, tbiase = net.b

but it doesn't work, weights don't initialize and it gives all 1 as result: twts =

    [2x2 double]
    []
    []
a = 
 1 1 1...1
 ...
 1 1 1...1
Is something wrong with layer connection? Or do I initialize something wrong?

Answer 1

[Vito]

No.

Multilayer percetron doesn't contain 'hardlim'(hardlim -is capable to classify only linearly separable set. Two or more layers in network - aren't separable linearly. ). Using 'logsig'.

The equivalent network - multilayer percetron.

P =[0 1 0 1; 0 0 1 1];
T = [0 0 0 1];

net=newff(minmax(P),[2,10,1],{'logsig','logsig','logsig'},'trainbfg');
net.trainParam.epochs = 100;
net = init (net);
net.IW{1,1}, net.IW{2,1}, 
  net.LW{3,2}
  net.b{1}, net.b{3}
net=train(net,P,T);
a = sim(net,P)

'trainbfg' – back propagation learning.

Error in network design.

[Greg Heath]

Typically, only one hidden layer is needed.

Use as many defaults as possible (help newff).

newff automatically initializes weights with initnw

.

if [I N] = size(p) and [O N] = size(t) then

there are Neq = N*O training equations and

Nw = (I+1)*H+(H+1)*O unknown weights. For

accurate weight estimation it is desired that

Neq >> Nw

Typically Neq >= 10*Nw is adequate. However,

sometimes a larger ratio (e.g., > 30) is needed

and sometimes a smaller ratio (e.g., 2) will suffice.

Hope this helps.

Greg