Training a Convolutional Autoencoder

I'm trying to train this simple convolutional autoencoder but I'm getting error on the training part. The error says the size of predictions and tragets are not the same. But When I check the network structure using the analyseNetwork function it seems that my input has the same size as my output. I can't find where is the error. Can someone help me?

Follows the code

datastore_MP = datastore("Tiles_MP1_100ov50\", "IncludeSubfolders",true, "LabelSource","foldernames");


images_MP = cell(numel(datastore_MP.Files), 1);

for i = 1:numel(datastore_MP.Files)
    img_MP = readimage(datastore_MP, i);
    [rows, cols] = size(img_MP);
    images_MP{i} = img_MP;
end



encoderBlock = @(block) [
    convolution2dLayer(3,2^(3+block), "Padding",'same')
    reluLayer
    maxPooling2dLayer(2,"Stride",2)
    convolution2dLayer(3,2^(5+block), "Padding",'same')
    reluLayer
    maxPooling2dLayer(2,"Stride",2)];
net_E = blockedNetwork(encoderBlock,1,"NamePrefix","encoder_");



decoderBlock = @(block) [
    transposedConv2dLayer(3,2^(5-block),"Stride",2)
    reluLayer
    transposedConv2dLayer(3,2^(1-block), "Stride",2)
    reluLayer];
net_D = blockedNetwork(decoderBlock,1,"NamePrefix","decoder_");


inputSize = [100 100 1];
CAE = encoderDecoderNetwork(inputSize,net_E,net_D);

analyzeNetwork(CAE)

options = trainingOptions( "adam",...
    "Plots","training-progress",...
    "MaxEpochs", 100,...
   "L2Regularization",0.001);

   
trainedCAE = trainnet(datastore_MP, CAE, "mse", options);

Answer 1

[newhere]

Hey, try changing 'trainnet' to 'trainNetwork'.

trainedCAE = trainNetwork(datastore_MP, CAE, "mse", options);

[Aghata]

Hello, Fatma.

I tried but this doesn't fix the problem. I've been trying to find a way to set the target size to be the same as the input or output, but without success.

Answer 2

[ali kaffashbashi]

I guess it tries to set your label sources (the folder names) as targets during the training. Hence, the input and output sizes become different. I reckon using the following code instead of your training line will solve your problem:

trainedCAE = trainnet(combine(datastore_MP,datastore_MP), CAE, "mse", options);