Invalid training data. The output size ([128 128 140 2]) of the last layer does not match the response size ([128 128 36 2]).

Dear All,

I have develop code for 3D U-Net. But got error when want train.

This is my code 3D U-Net. The Data can get thru this link https://drive.google.com/drive/folders/1cbKwpP8P8oblAs4_geDpRUcg8Y3IEu_j?usp=share_link

clc
clear all
close all
%testDataimages
DATASetDir = fullfile('C:\Users\USER\Downloads\HEAD & NECK\HEAD & NECK');
IMAGEDir = fullfile(DATASetDir,'ImagesTr');
volReader = @(x) matRead(x);
volds = imageDatastore(IMAGEDir, ...
    'FileExtensions','.mat','ReadFcn',volReader);
% labelReader = @(x) matread(x);
matFileDir = fullfile('C:\Users\USER\Downloads\HEAD & NECK\HEAD & NECK\LabelsTr');
classNames = ["background", "tumor"];
pixelLabelID = [0 1];
% pxds = (LabelDirr,classNames,pixelLabelID, ...
%     'FileExtensions','.mat','ReadFcn',labelReader);
pxds = pixelLabelDatastore(matFileDir,classNames,pixelLabelID, ...
    'FileExtensions','.mat','ReadFcn',@matRead);
volume = preview(volds);
label = preview(pxds);
volumeViewer(volume, label)
patchSize = [128 128 36];
patchPerImage = 16;
miniBatchSize = 8;
patchds = randomPatchExtractionDatastore(volds,pxds,patchSize, ...
    'PatchesPerImage',patchPerImage);
patchds.MiniBatchSize = miniBatchSize;
dsTrain = transform(patchds,@augment3dPatch);
volLocVal = fullfile('C:\Users\USER\Downloads\HEAD & NECK\HEAD & NECK\ImagesVal');
voldsVal = imageDatastore(volLocVal, ...
    'FileExtensions','.mat','ReadFcn',volReader);
lblLocVal = fullfile('C:\Users\USER\Downloads\HEAD & NECK\HEAD & NECK\LabelsVal');
pxdsVal = pixelLabelDatastore(lblLocVal,classNames,pixelLabelID, ...
    'FileExtensions','.mat','ReadFcn',volReader);
dsVal = randomPatchExtractionDatastore(voldsVal,pxdsVal,patchSize, ...
    'PatchesPerImage',patchPerImage);
dsVal.MiniBatchSize = miniBatchSize;
lgraph = layerGraph();
tempLayers = [
    image3dInputLayer([128 128 36 1],"Name","image3dinput")
    convolution3dLayer([3 3 3],64,"Name","Encoder-Stage-1-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Encoder-Stage-1-ReLU-1")
    convolution3dLayer([3 3 3],64,"Name","Encoder-Stage-1-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Encoder-Stage-1-ReLU-2")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    maxPooling3dLayer([2 2 2],"Name","Encoder-Stage-1-MaxPool","Stride",[2 2 2])
    convolution3dLayer([3 3 3],128,"Name","Encoder-Stage-2-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Encoder-Stage-2-ReLU-1")
    convolution3dLayer([3 3 3],128,"Name","Encoder-Stage-2-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Encoder-Stage-2-ReLU-2")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    maxPooling3dLayer([2 2 2],"Name","Encoder-Stage-2-MaxPool","Stride",[2 2 2])
    convolution3dLayer([3 3 3],256,"Name","Encoder-Stage-3-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Encoder-Stage-3-ReLU-1")
    convolution3dLayer([3 3 3],256,"Name","Encoder-Stage-3-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Encoder-Stage-3-ReLU-2")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    dropoutLayer(0.5,"Name","Encoder-Stage-3-DropOut")
    maxPooling3dLayer([2 2 2],"Name","Encoder-Stage-3-MaxPool","Stride",[2 2 2])
    convolution3dLayer([3 3 3],512,"Name","Bridge-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Bridge-ReLU-1")
    convolution3dLayer([3 3 3],512,"Name","Bridge-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Bridge-ReLU-2")
    dropoutLayer(0.5,"Name","Bridge-DropOut")
    transposedConv3dLayer([2 2 2],256,"Name","Decoder-Stage-1-UpConv","BiasLearnRateFactor",2,"Stride",[2 2 2],"WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-1-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    depthConcatenationLayer(2,"Name","Decoder-Stage-1-DepthConcatenation")
    convolution3dLayer([3 3 3],256,"Name","Decoder-Stage-1-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-1-ReLU-1")
    convolution3dLayer([3 3 3],256,"Name","Decoder-Stage-1-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-1-ReLU-2")
    transposedConv3dLayer([2 2 2],128,"Name","Decoder-Stage-2-UpConv","BiasLearnRateFactor",2,"Stride",[2 2 2])
    reluLayer("Name","Decoder-Stage-2-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    depthConcatenationLayer(2,"Name","Decoder-Stage-2-DepthConcatenation")
    convolution3dLayer([3 3 3],128,"Name","Decoder-Stage-2-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-2-ReLU-1")
    convolution3dLayer([3 3 3],128,"Name","Decoder-Stage-2-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-2-ReLU-2")
    transposedConv3dLayer([2 2 2],64,"Name","Decoder-Stage-3-UpConv","BiasLearnRateFactor",2,"Stride",[2 2 2],"WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-3-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    depthConcatenationLayer(2,"Name","Decoder-Stage-3-DepthConcatenation")
    convolution3dLayer([3 3 3],64,"Name","Decoder-Stage-3-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-3-ReLU-1")
    convolution3dLayer([3 3 3],64,"Name","Decoder-Stage-3-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-3-ReLU-2")
    convolution3dLayer([1 1 1],2,"Name","Final-ConvolutionLayer","Padding","same","WeightsInitializer","he")
    softmaxLayer("Name","Softmax-Layer")
    pixelClassificationLayer("Name","Segmentation-Layer")];
lgraph = addLayers(lgraph,tempLayers);
% clean up helper variable
clear tempLayers;
lgraph = connectLayers(lgraph,"Encoder-Stage-1-ReLU-2","Encoder-Stage-1-MaxPool");
lgraph = connectLayers(lgraph,"Encoder-Stage-1-ReLU-2","Decoder-Stage-3-DepthConcatenation/in2");
lgraph = connectLayers(lgraph,"Encoder-Stage-2-ReLU-2","Encoder-Stage-2-MaxPool");
lgraph = connectLayers(lgraph,"Encoder-Stage-2-ReLU-2","Decoder-Stage-2-DepthConcatenation/in2");
lgraph = connectLayers(lgraph,"Encoder-Stage-3-ReLU-2","Encoder-Stage-3-DropOut");
lgraph = connectLayers(lgraph,"Encoder-Stage-3-ReLU-2","Decoder-Stage-1-DepthConcatenation/in2");
lgraph = connectLayers(lgraph,"Decoder-Stage-1-UpReLU","Decoder-Stage-1-DepthConcatenation/in1");
lgraph = connectLayers(lgraph,"Decoder-Stage-2-UpReLU","Decoder-Stage-2-DepthConcatenation/in1");
lgraph = connectLayers(lgraph,"Decoder-Stage-3-UpReLU","Decoder-Stage-3-DepthConcatenation/in1");
plot(lgraph);
options = trainingOptions('sgdm', ...
    'MiniBatchSize',1, ...
    'MaxEpochs',100, ...
    'InitialLearnRate',1e-3, ...
    'Shuffle','every-epoch', ...
    'ValidationData',dsVal, ...
    'ValidationFrequency',200, ...
    'Verbose',false, ...
    'Plots','training-progress', ...
    'ExecutionEnvironment','cpu');
doTraining = true;
if doTraining
    modelDateTime = datestr(now,'dd-mmm-yyyy-HH-MM-SS');
    [net,info] = trainNetwork(dsTrain,lgraph,options);
    save(['trained3DUNet-' modelDateTime '-Epoch-' num2str(maxEpochs) '.mat'],'net');
else
    load('trained3DVNet-07-Jun-2022-13-45-30-Epoch-250.mat');
end

This is my ERROR

Error using trainNetwork
Invalid training data. The output size ([128 128 140 2]) of the last layer does not match the response size ([128 128 36
2]).

[Walter Roberson]

How many different classes do you have?

The implication of the error message is that you have 140 different classes. To train on 140 different classes, your output size needs to have 140 as a component.

[mohd akmal masud]

No, I just only 2 classes

classNames = ["background", "tumor"];
pixelLabelID = [0 1];

[Amanjit Dulai]

The labels are being loaded with size [128 128 140 2]. This is being done by the function matRead which is specified as an input when creating the variable pxds. But this function is not included in your code above. Do you have the code for this function?

[mohd akmal masud]

no, i dont have