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

Question

mohd akmal masud 2022-11-8

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1845838-invalid-training-data-the-output-size-128-128-140-2-of-the-last-layer-does-not-match-the-respon

评论： mohd akmal masud 2022-11-9

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]).

4 个评论
显示 2更早的评论隐藏 2更早的评论

Amanjit Dulai 2022-11-8

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 2022-11-9