Hi
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. For reference you can check this documentation for that.
For custom loss function, you can check this documentation.
Hope it will help!
