A single hidden layered NN but the weight matrix is a diagonal matrix (with a bias) -- how to design that?

You can set any values to the above weights and set the net.layerWeights{i,j}.learn to 0 so that the weights won't be altered during the training & adaption. In this case setting a specific weight for a connection is not possible since the property net.layerWeights{i,j}.learn is defined for the entire connections between layers i and j.

net.layerWeights{i,j}.learn = 0
net.LW{i,j} = ones(size(net.LW{i,j})) % any weights of size(net.LW{i,j})

If your network architecture is defined and trained already:

Then you can set weight of a connection between nodes k & l of layers i & j as follows:

net.LW{i,j}(k,l) = 1

and then use the network.

The above things can be done to Input wieghts too.

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Srivardhan Gadila 2020-5-29

编辑：Srivardhan Gadila 2020-5-29

@Shashi Kant, as per my knowledge on shallow neural networks:

I listed the possible things you can do w.r.t the weights of layers of shallow nerual networks in the Answer.
The property net.layerWeights{i,j}.learn is defined for the entire connections between layers i and j hence you cannot set the diagonal weights to learn only & non-diagonal weights to not learn.
You can instead define custom Deep Learning layer to achieve your functionality. Refer to Define Custom Deep Learning Layers & Define Custom Deep Learning Layer with Learnable Parameters

Shashi Kant 2020-5-29

@Srivardhan Gadila Thank you for your suggestion. I will look into that. I am not very familar with the deep learning toolbox. But I will dig into it.

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