Architecture of convolutional autoencoders in Matlab 2019b

Question

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,

John Williams · Accepted Answer

You can define custom architecture of auoencoder using deep learning layers. You can refer to  this documentation for the list of deep learning layers supported in MATLAB. For example, the autoencoder network can be defined as:
 
 
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.

Architecture of convolutional autoencoders in Matlab 2019b

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