How to add new properties (layer name) in DlconvOp.m in deep network training

Hi,

Thanks for the new feature of custom layer backward function which allow me to add my new functions during the training of deep convolutional neural network. In the DlconvOp.m file, there are for properties {paddingSize,stride,dilation,numGroups}; How to add a new property such as current layer index or layer name in the function so I can update my weights according?

Answer 1

[Srivardhan Gadila]

"Name" is already a property of the custom layer whereas you can define the "Index" property under the Optional Properties. You can do something like below:

classdef myLayer < nnet.layer.Layer

    properties
        % (Optional) Layer properties.
        LayerIndex

        % Layer properties go here.
    end

    properties (Learnable)
        % (Optional) Layer learnable parameters.

        % Layer learnable parameters go here.
    end
    
    methods
        function layer = myLayer(layerName, layerIndex)
            % (Optional) Create a myLayer.
            % This function must have the same name as the class.

            % Layer constructor function goes here.
            layer.Name = layerName;
            layer.LayerIndex = layerIndex;
        end
        
        function [Z1, …, Zm] = predict(layer, X1, …, Xn)
            
            % Layer forward function for prediction goes here.
        end

        function [dLdX1, …, dLdXn, dLdW1, …, dLdWk] = ...
                backward(layer, X1, …, Xn, Z1, …, Zm, dLdZ1, …, dLdZm, memory)
        % (Optional) Backward propagate the derivative of the loss  
        % function through the layer.

        end
    end
end

And use it as follows:

MyLayer = myLayer("MyCustomLayer",1)

You can refer to Define Custom Deep Learning Layer with Learnable Parameters for more information.