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

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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?

Accepted Answer

Srivardhan Gadila
Srivardhan Gadila on 10 Feb 2021
"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.

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