Error trainNetwork for U net Deep learning.

조회 수: 4 (최근 30일)
mohd akmal masud
mohd akmal masud 2022년 5월 31일
답변: Sanjana 2023년 9월 1일
Dear All,
I tried to train U-Net Network Deep Learning. data for IMAGEDir(256x256x24single.mat) and matFileDir(256x256x24singlecategorical.mat) as attached.
below is my coding
clc
clear all
close all
%testDataimages
DATASetDir = fullfile('C:\Users\Akmal\Desktop\Unet3D');
IMAGEDir = fullfile(DATASetDir,'256x256x24singlemat');
volReader = @(x) matRead(x);
volds = imageDatastore(IMAGEDir, ...
'FileExtensions','.mat','ReadFcn',volReader);
% labelReader = @(x) matread(x);
matFileDir = fullfile('C:\Users\Akmal\Desktop\Unet3D');
classNames = ["background", "foreground"];
pixelLabelID = [1 2];
% pxds = (LabelDirr,classNames,pixelLabelID, ...
% 'FileExtensions','.mat','ReadFcn',labelReader);
pxds = pixelLabelDatastore(matFileDir,classNames,pixelLabelID, ...
'FileExtensions','.mat','ReadFcn',@matRead);
volume = preview(volds);
label = preview(pxds);
ds = pixelLabelImageDatastore(volds,pxds);
tbl = countEachLabel(pxds)
totalNumberOfPixels = sum(tbl.PixelCount);
frequency = tbl.PixelCount / totalNumberOfPixels;
inverseFrequency = 1./frequency
layerf=pixelClassificationLayer("Name","Segmentation-Layer")
lgraph = layerGraph();
tempLayers = [
image3dInputLayer([128 128 128 3],"Name","ImageInputLayer")
convolution3dLayer([3 3 3],16,"Name","Encoder-Stage-1-Conv-1","Padding","same","WeightsInitializer","he")
batchNormalizationLayer("Name","Encoder-Stage-1-BN-1")
reluLayer("Name","Encoder-Stage-1-ReLU-1")
convolution3dLayer([3 3 3],32,"Name","Encoder-Stage-1-Conv-2","Padding","same","WeightsInitializer","he")
batchNormalizationLayer("Name","Encoder-Stage-1-BN-2")
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],32,"Name","Encoder-Stage-2-Conv-1","Padding","same","WeightsInitializer","he")
batchNormalizationLayer("Name","Encoder-Stage-2-BN-1")
reluLayer("Name","Encoder-Stage-2-ReLU-1")
convolution3dLayer([3 3 3],64,"Name","Encoder-Stage-2-Conv-2","Padding","same","WeightsInitializer","he")
batchNormalizationLayer("Name","Encoder-Stage-2-BN-2")
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],64,"Name","Bridge-Conv-1","Padding","same","WeightsInitializer","he")
batchNormalizationLayer("Name","Bridge-BN-1")
reluLayer("Name","Bridge-ReLU-1")
convolution3dLayer([3 3 3],128,"Name","Bridge-Conv-2","Padding","same","WeightsInitializer","he")
batchNormalizationLayer("Name","Bridge-BN-2")
reluLayer("Name","Bridge-ReLU-2")
transposedConv3dLayer([2 2 2],128,"Name","Decoder-Stage-1-UpConv","BiasLearnRateFactor",2,"Stride",[2 2 2],"WeightsInitializer","he")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
concatenationLayer(4,2,"Name","Decoder-Stage-1-Concatenation")
convolution3dLayer([3 3 3],64,"Name","Decoder-Stage-1-Conv-1","Padding","same","WeightsInitializer","he")
batchNormalizationLayer("Name","Decoder-Stage-1-BN-1")
reluLayer("Name","Decoder-Stage-1-ReLU-1")
convolution3dLayer([3 3 3],64,"Name","Decoder-Stage-1-Conv-2","Padding","same","WeightsInitializer","he")
batchNormalizationLayer("Name","Decoder-Stage-1-BN-2")
reluLayer("Name","Decoder-Stage-1-ReLU-2")
transposedConv3dLayer([2 2 2],64,"Name","Decoder-Stage-2-UpConv","BiasLearnRateFactor",2,"Stride",[2 2 2],"WeightsInitializer","he")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
concatenationLayer(4,2,"Name","Decoder-Stage-2-Concatenation")
convolution3dLayer([3 3 3],32,"Name","Decoder-Stage-2-Conv-1","Padding","same","WeightsInitializer","he")
batchNormalizationLayer("Name","Decoder-Stage-2-BN-1")
reluLayer("Name","Decoder-Stage-2-ReLU-1")
convolution3dLayer([3 3 3],32,"Name","Decoder-Stage-2-Conv-2","Padding","same","WeightsInitializer","he")
batchNormalizationLayer("Name","Decoder-Stage-2-BN-2")
reluLayer("Name","Decoder-Stage-2-ReLU-2")
convolution3dLayer([1 1 1],5,"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-2-Concatenation/in2");
lgraph = connectLayers(lgraph,"Encoder-Stage-2-ReLU-2","Encoder-Stage-2-MaxPool");
lgraph = connectLayers(lgraph,"Encoder-Stage-2-ReLU-2","Decoder-Stage-1-Concatenation/in2");
lgraph = connectLayers(lgraph,"Decoder-Stage-1-UpConv","Decoder-Stage-1-Concatenation/in1");
lgraph = connectLayers(lgraph,"Decoder-Stage-2-UpConv","Decoder-Stage-2-Concatenation/in1");
plot(lgraph);
imageSize = [64 64 64];
numClasses = 2;
encoderDepth = 3;
lgraph = unetLayers(imageSize,numClasses,'EncoderDepth',encoderDepth)
options1 = trainingOptions('adam', ...
'InitialLearnRate',1e-3, ...
'MaxEpochs',20, ...
'LearnRateDropFactor',5e-1, ...
'LearnRateDropPeriod',20, ...
'LearnRateSchedule','piecewise', ...
'MiniBatchSize',4,'Plots','training-progress');
net1 = trainNetwork(ds,lgraph,options1);
But I got Error like below. anyone can help me?
Error using trainNetwork (line 184)
Invalid training or validation response data. Categorical responses must either
be a vector or a single-channel 2-D or 3-D image.

답변 (1개)

Sanjana
Sanjana 2023년 9월 1일
Hi Mohd,
I understand that you are facing an issue with “trainNetwork” function while training the U-Net with the input Image data being labeled with the categorical data.
The “Categorical” data should be a 2 Dimensional “Categorical array”, whereas the categorical data you provided is a 3-dimensional “Categorical” array.
For the “256x256x24” Input data, the label data should be a “256x24” “Categorical array”. You can use the following code to modify the Categorical label data to the above dimensions,
label_p = permute(label,[1,3,2])
label_mod = label_p(:,:,1);
Please refer to the following link containing an example on Segmentation using deep learning, where you can understand how training and testing data are generated for the Input data,
Hope this helps!
Regards,
Sanjana

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