MATLAB: How to Show the Weight or Bias in a Neural Network

inputneural networkoutput

How to show the weight/bias from every layer in my neural network? I am doing a feedforward neural network with 2 hidden layers. Furthermore, how to determine how many hidden layers should I use in a neural network? Currently I have 3 inputs and 1 output. When I want to increase the hidden layer to 3, an error occurred saying that I have not sufficient of input for 3 hidden layers.

Best Answer

1. If the input/output transformation function is reasonably well behaved, 1 hidden layer is sufficient. The resulting net is a universal approximator.
2. However, if you need a ridiculously high number of hidden nodes, H, ( especially if the number of unknown weights Nw = (I+1)*H+(H+1)*O approaches or exceeds the number of training equations Ntrneq = Ntrn*O), you can reduce the total number of nodes by introducing a second hidden layer.
 [ I Ntrn ] = size(trninput) 
 [ O Ntrn  ] = size(trntarget)
3. Nevertheless, it is usually better to stick with 1 hidden layer and use a validation stopping subset (the default) and/or a regularized objective function (an option of mse: help mse) or a regularization training function (help trainbr)
4. Sometimes a ridiculously high number of weights is the result of using a ridiculously high number of inputs. So, it may be worthwhile to consider input subset selection before resorting to a second hidden layer.
For a single hidden layer
 weights = getwb(net)
         = [ Iw(:); b1(:); Lw(:); b2(:) ]
where
 Iw = cell2net(net.IW)
 b1  = cell2mat(net.b(1))
 Lw = cell2mat(net.Lw)
 b2 = cell2mat(net.b(2))
You can try an example if you want to see how getwb orders weights with 2 hidden layers.
Hope this helps.
Thank you for formally accepting my answer
Greg
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