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How to "prune" a one hidden layer NN such that the off diagonal elements of the weights, that is, "net.IW{1}" and "net.LW{2}", are zeros?

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Shashi Kant
Shashi Kant on 23 May 2020
Answered: Abdelwahab Afifi on 12 Apr 2021 at 18:45
How can I create a single hidden layered NN such that the off main diagonal elements of the weights, that is, "net.IW{1}" and "net.LW{2}", are zeros?
Please see my partial script below with a question there. Can someone help me with this toy example?
%% Create a dataset with a network
[x,t] = crab_dataset; %load the dataset
x = x(1:2,:); % Input length
net = patternnet(2); %create the network
net = configure(net,x,t);
view(net);
w1 = net.IW{1}; %the input-to-hidden layer weights (but w1
w2 = net.LW{2}; %the hidden-to-output layer weights
b1 = net.b{1}; %the input-to-hidden layer bias
b2 = net.b{2}; %the hidden-to-output layer bias
%% QUESTION (or INTENTION):
% How to setup the net such that the off diagonal elements of w1 or net.IW{1} and w2 or net.LW{2} are ZEROs after training the network?
%% then, I would like to train the network with my defined network!
% [net,tr] = train(net,x,t); %train the network

Answers (2)

Srivardhan Gadila
Srivardhan Gadila on 29 May 2020
As per my knowledge w.r.t shallow nerual networks you cannot freeze non-diagonal weights & make the diagonal wieghts only to update since the property net.layerWeights{i,j}.learn is defined for the entire connections between layers i and j.
I would suggest you to use Deep Nerual networks instead of shallow nerual networks & define custom Deep Learning layer to achieve your functionality. Refer to Define Custom Deep Learning Layers & Define Custom Deep Learning Layer with Learnable Parameters
Other suggestion w.r.t shallow nerual networks approach: (may or may not be useful)
Set the net.trainParam.epochs to 1 & place the [net,tr] = train(net,x,t); in a for loop iterated over total number of epochs, then after each epoch set the non-diagonal weights to zero.
  1 Comment
Shashi Kant
Shashi Kant on 29 May 2020
@Srivardhan Gadila: Thank you for your comment. Would it be possible for you to create a simple example with the DNN toolbox? I would greatly appreciate it.

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