dlnetwork regression example in matlab

main.m
% Load the data
load housing_dataset
X = housing{:, 4:19};
Y = housing{:, 3};

% Split data into training and testing sets
cv = cvpartition(size(X, 1), 'HoldOut', 0.3); 
XTrain = X(cv.training,:);
YTrain = Y(cv.training,:);
XTest = X(cv.test,:);
YTest = Y(cv.test,:);

% Define the dlnetwork
numFeatures = size(XTrain, 2);
numHiddenUnits = 10;
numResponses = 1;
layers = [ ...
    sequenceInputLayer(numFeatures)
    fullyConnectedLayer(numHiddenUnits)
    reluLayer
    fullyConnectedLayer(numResponses)
    regressionLayer];

% Set options for training the network
options = trainingOptions('adam', ...
    'MaxEpochs', 500, ...
    'MiniBatchSize', 64, ...
    'InitialLearnRate', 0.001, ...
    'Shuffle', 'every-epoch', ...
    'Verbose', false);

% Train the network
net = trainNetwork(XTrain', YTrain', layers, options);

% Test the network
YPred = predict(net, XTest');
rmse = sqrt(mean((YPred - YTest').^2));
fprintf('Root Mean Squared Error = %.2f\n', rmse);
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