Architecture of convolutional autoencoders in Matlab 2019b

bright_hill - 2021-05-12T12:15:00+00:00

Question: Architecture of convolutional autoencoders in Matlab 2019b

am very interested in training convolutional autoencoders in MATLAB 2019b. I have found the instruction trainAutoencoder, but it does not allow to specify the convolutional layers architecture. I want to design my autoencoder using Deep Network Designer tool, and then train it just as it is done with CNNs, FasterRCNN algorithms, etc, using my image dataset. Is this possible? Is there any code oxample out there to do so? Thank you all in advance,

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Expert Answer

John Williams answered . 2025-11-20

layers=[
    imageInputLayer(size,"Name","imageinput",'Normalization','none')  %size is the size of input
    fullyConnectedLayer(9*R,"Name","fc_1")    %R can be any number/ factor
    leakyReluLayer(0.01,"Name","leakyrelu_1")
    fullyConnectedLayer(6*R,"Name","fc_2")
    leakyReluLayer(0.01,"Name","leakyrelu_3")
    fullyConnectedLayer(3*R,"Name","fc_3")
    leakyReluLayer(0.01,"Name","leakyrelu_4")
    fullyConnectedLayer(R,"Name","fc_4")
    batchNormalizationLayer("Name","batchnorm")
    reluLayer('Name','relu1')
    dropoutLayer('Name','drop')
    fullyConnectedLayer(3*R,"Name","fc_4")
    leakyReluLayer(0.01,"Name","leakyrelu_5")
    fullyConnectedLayer(6*R,"Name","fc_4")
    leakyReluLayer(0.01,"Name","leakyrelu_6")
    fullyConnectedLayer(9*R,"Name","fc_4")
    leakyReluLayer(0.01,"Name","leakyrelu_7")
    fullyConnectedLayer(size,"Name","fc_5")
    leakyReluLayer(0.01,"Name","leakyrelu_8")
    regressionLayer("Name","regressionoutput")];

You can use 2D / 3D conv layer/ any other layer as per your architecture. After defining the network, you can train the model.

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