Error trainNetwork for U net Deep learning.

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.

Answer 1

[Sanjana]

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,

https://www.mathworks.com/help/vision/ug/semantic-segmentation-using-deep-learning.html

Hope this helps!

Regards,

Sanjana