Hi Jeffrey,
As of now, MATLAB's Deep Learning Toolbox does not have a built-in “squaredLayer”. However, you can create a custom layer to perform element-wise squaring. Follow the steps below to define a custom layer that squares its input and integrates it into a simple neural network:
Step 1: Create a custom class named “SquaredLayer”. Save this class in a file with the same name, “SquaredLayer.m”.
classdef SquaredLayer < nnet.layer.Layer
% Custom layer to perform element-wise squaring
methods
function layer = SquaredLayer(name)
% Create a SquaredLayer
layer.Name = name;
layer.Description = 'Element-wise square layer';
end
function Z = predict(layer, X)
% Forward input data through the layer at prediction time
Z = X .^ 2;
end
function [dLdX] = backward(layer, X, Z, dLdZ, memory)
% Backward propagate the derivative of the loss function
dLdX = 2 * X .* dLdZ;
end
end
end
Step 2: Define a neural network and while defining the layers you could use this custom class, in this case I have used built in classes “digitTrain4DArrayData” and “digitTest4DArrayData” provided by MATLAB.
% Load example data
[XTrain,YTrain,anglesTrain] = digitTrain4DArrayData;
[XTest,YTest,anglesTest] = digitTest4DArrayData;
% Define the layers of the network
layers = [
imageInputLayer([28 28 1], 'Name', 'input') % Example input layer for 28x28 grayscale images
SquaredLayer('squared') % Custom squared layer
fullyConnectedLayer(10, 'Name', 'fc')
softmaxLayer('Name', 'softmax')
classificationLayer('Name', 'output')
];
% Specify training options
options = trainingOptions('adam', ...
'MaxEpochs', 10, ...
'InitialLearnRate', 0.01, ...
'Verbose', false, ...
'Plots', 'training-progress');
% Train the network
net = trainNetwork(XTrain, YTrain, layers, options);
% Test the network on the test data
YPred = classify(net, XTest);
accuracy = sum(YPred == YTest) / numel(YTest);
disp(['Test accuracy: ', num2str(accuracy)]);
You can refer to the documentation below for more information: Define Custom Deep Learning Layer with Multiple Inputs: https://www.mathworks.com/help/releases/R2021a/deeplearning/ug/define-custom-layer-with-multiple-inputs.html
