MATLAB: Knowing the Weights in Matlab

Deep Learning ToolboxMATLABneural network

I fitted the function using neural networks and after the computation is complete, I want to know the Final Weights and bias used after the optimisation process is complete. when I used the net.b command it is showing an array…which is not clear in terminology (P.S. net is name of my file)

Best Answer

The biases for each layer i are net.b{i}. So for a two layer network the biases are net.b{1} and net.b{2}.

The weights to layer i from input j are net.IW{i,j}. For a typical two layer network net.IW{1,1} will exist, while net.IW{2,1} will be empty because the input only goes to layer 1.

The weights to layer i from layer j are net.LW{i,j}. For a typical two layer network net.LW{2,1} will contain the weights to layer 2 from layer 1 and the other layer weights will be empty.

You should also take into account the input and output processing, if you are wanting to reproduce the input-output network function yourself. These functions and settings are available for a two layer network with these properties:

net.inputs{1}.processFcns
net.inputs{1}.processSettings
net.outputs{2}.processFcns
net.outputs{2}.processSettings

Related Solutions

MATLAB: How to create a neural network with 1 layer only (no hidden layers)

 net = fitnet([]);     % For regression
 net = patternnet([]); % For classification

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: Neural network weight and bias initializaiton problem

 net                        = patternnet(10);
 net.inputs{1}.processFcns  = {};
 net.outputs{2}.processFcns = {};
 net                        = configure(net,INPUT,Target);
 net                        = setwb(net,Weights);
 IW                         = net.IW
 b                          = net.b
 LW                         = net.LW