matlab variational autoencoder in matlab

main.m
% MATLAB code for implementing a Variational Autoencoder (VAE)

% Step 1: Define the encoder network
encoder = [
    imageInputLayer([28 28 1])
    convolution2dLayer(3, 8, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    convolution2dLayer(3, 16, 'Padding', 'same', 'Stride', 2)
    batchNormalizationLayer
    reluLayer
    convolution2dLayer(3, 32, 'Padding', 'same', 'Stride', 2)
    batchNormalizationLayer
    reluLayer
    fullyConnectedLayer(64)
    reluLayer
    fullyConnectedLayer(20)
];

% Step 2: Define the decoder network
decoder = [
    fullyConnectedLayer(64)
    reluLayer
    fullyConnectedLayer(7*7*32)
    reluLayer
    reshapeLayer([7 7 32])
    transposedConv2dLayer(3, 16, 'Stride', 2, 'Cropping', 'same')
    batchNormalizationLayer
    reluLayer
    transposedConv2dLayer(3, 8, 'Stride', 2, 'Cropping', 'same')
    batchNormalizationLayer
    reluLayer
    transposedConv2dLayer(3, 1, 'Cropping', 'same')
    sigmoidLayer
    regressionLayer
];

% Step 3: Combine the encoder and decoder to form the VAE
vae = variationalAutoencoder(encoder, decoder);

% Step 4: Train the VAE model
opts = trainingOptions('adam', ...
    'MaxEpochs', 50, ...
    'MiniBatchSize', 128, ...
    'Plots', 'training-progress');
XTrain = digitTrainCellArrayData;
vae = trainNetwork(XTrain, [], vae, opts);

% Step 5: Generate new samples using the trained VAE
Z = randn([1 20]);
XGen = predict(vae, Z);
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