How to implement nguyen widrow weight initialization ?

Hi, I'm interested to use nguyen widrow weight initialization but i do not want to use the existing matlab function. I have the pseudo code and I want to try coding it myself. One thing I'm not sure on is how the technique calculate the norm of weights ? suppose I have input weights of [5x3] do i calculate the norm of entire matrix or column ? Thanks in advance

Answer 1

[Ênio Viana]

I want to know this too. Thanks in advance

Answer 2

[Ênio Viana]

In this link you can see a Matlab code implementation Stack Overflow but I didn't test this. See that code...

a = -1;
b = 1;

% WIDROW weights for Layer Input to Hidden Layer 1

sum_sq_wts = 0;

for k=1:30
    iw(:,:) = zeros(num_input, nodes_hidden_layer);
    for i=1:num_input
        for j=1:nodes_hidden_layer
            iw(i,j)=(b-a)*rand(1,1) + a;
            sum_sq_wts = sum_sq_wts + (iw(i,j)*iw(i,j));
         end
    end

      norm = sqrt(sum_sq_wts);
      beta = 0.7*nodes_hidden_layer.^(1/num_input);

      for i=1:num_input
          for j=1:nodes_hidden_layer
              iw(i,j) = beta*iw(i,j)/norm;
          end
      end
      IW{k}=iw';
  end

% WIDROW weights for Hidden Layer 1 to output Layer

sum_sq_wts = 0;
for k=1:30
    lw(:,:) = zeros(nodes_hidden_layer,1);
    for i=1:nodes_hidden_layer
        for j=1:1
            iw(i,j)=(b-a)*rand(1,1) + a;
            sum_sq_wts = sum_sq_wts + iw(i,j)*iw(i,j);
        end
    end

      norm = sqrt(sum_sq_wts);
      beta = 0.7*nodes_hidden_layer.^(1/num_input);

      for i=1:nodes_hidden_layer
          for j=1:1
              lw(i,j) = beta*lw(i,j)/norm;
          end
      end

      LW{k}=lw';
  end

WidNgu{1,1} = IW;
WidNgu{1,2} = LW;

[crixus]

not sure if it's like this

InputWeight=rand(NumberofHiddenNeurons,NumberofInputNeurons)*2-1; beta = 0.7*(NumberofHiddenNeurons^(1/NumberofInputNeurons)); InputWeight = InputWeight/norm(InputWeight) * beta;

xmin=min(min(InputWeight)); xmax=max(max(InputWeight)); BiasofHiddenNeurons=xmin+rand(NumberofHiddenNeurons,1)*(xmax-xmin);