K A Sundararaman To write the MATLAB code for deep learning with PSO optimization, you should first become acquainted with the fundamental ideas of deep learning and PSO. Online tutorials and resources might assist you in understanding how to apply these algorithms in MATLAB. Once you grasp the fundamental ideas, you may begin developing your own code or adapting existing code to meet your individual requirements.

You might also go online for MATLAB code snippets or examples that show how to optimize weights and biases using PSO. Existing code may be available on websites such as MATLAB Central, GitHub, and the MathWorks File Exchange.

Please bear in mind that creating code from scratch can be a hard and time-consuming effort, and a working implementation may need a considerable understanding of both deep learning and PSO.

K A Sundararaman

Hi, I have just shared the code, you can change as per your need.

% Load the data

load('data.mat');

% Define the network architecture

layers = [ imageInputLayer([28 28 1])

convolution2dLayer(3,32,'Padding','same')

reluLayer

maxPooling2dLayer(2,'Stride',2)

fullyConnectedLayer(10)

softmaxLayer

classificationLayer];

% Define the optimization function

fun = @(params)deepLearningObjective(params,layers,XTrain,TTrain);

% Define the PSO options

options = optimoptions(@particleswarm,'Display','iter',...

'UseParallel',true,'MaxIterations',50,'FunctionTolerance',1e-6);

% Optimize the weights and biases using PSO

params = particleswarm(fun,numel(layers.learnables),[],[],[],[],[],[],[],options);

% Use the optimized weights and biases to make predictions

layers = setParams(layers,params);

YTrainPred = predict(layers,XTrain);

YValidationPred = predict(layers,XValidation);

% Evaluate the accuracy of the predictions

trainAccuracy = mean(YTrain == YTrainPred);

validationAccuracy = mean(YValidation == YValidationPred);

fprintf('Train Accuracy: %0.4f\nValidation Accuracy: %0.4f\n',trainAccuracy,validationAccuracy);