how to validate data trainned

Hi all,

Anyone know how to validate the data trainned?

Because before this, I just trainned what I labelled using groundTruthLabeler. Then how to validate data (in red rectangle) that we have??

Answer 1

[yanqi liu]

yes，sir，may be set options，such as

options = trainingOptions('sgdm', ...
    'MaxEpochs',100, ...
    'GradientThreshold',1, ...
    'Verbose',false, ...
    'ValidationData',{XVal, YVal},...
    'LearnRateDropFactor',0.2,...
    'LearnRateDropPeriod',5,...
    'Plots','training-progress');

[mohd akmal masud]

sir, this is my code. Then where shoud I put your code suggestion?

%% first, read the image data and labelled images
clc
clear all
dataSetDir = fullfile('C:\Users\Akmal\Desktop\I-131 256 28.02.2020\I-131 SPECT NEMA VALIDATION 01112019 256X256 26.09.2021 petang');
imageDir = fullfile(dataSetDir,'Image');
labelDir = fullfile(dataSetDir,'PixelLabelData');

imds = imageDatastore(imageDir);

% view data set images origional
figure
for i = 1:23
    subplot(5,5,i)
    I = readimage(imds,i);
    imshow(I)
    title('training labels')
end


%% train the data. if network already, then just drag it into command window
classNames = ["foreground" "background"];
labelIDs = [1 2];
pxds = pixelLabelDatastore(labelDir, classNames, labelIDs);

imds1 = imageDatastore(labelDir);
% figure
% for i = 1:5
%     subplot(3,3,i)
%     I = readimage(imds1,i);
%     imshow(I)
%     title('training labels')
% end

ds = pixelLabelImageDatastore(imds,pxds);

tbl = countEachLabel(pxds)

totalNumberOfPixels = sum(tbl.PixelCount);
frequency = tbl.PixelCount / totalNumberOfPixels;
inverseFrequency = 1./frequency

%    layerf = pixelClassificationLayer(...
%       'Classes',tbl.Name,'ClassWeights',inverseFrequency)
%   
layerf=pixelClassificationLayer("Name","Segmentation-Layer")

lgraph = layerGraph();

tempLayers = [
    
imageInputLayer([512/2 512/2 1],"Name","ImageInputLayer")

convolution2dLayer([4 4],64,"Name","Encoder-Stage-1-Conv-1","Padding","same","WeightsInitializer","he")
reluLayer("Name","Encoder-Stage-1-ReLU-1")
convolution2dLayer([4 4],64,"Name","Encoder-Stage-1-Conv-2","Padding","same","WeightsInitializer","he")
reluLayer("Name","Encoder-Stage-1-ReLU-2")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
    maxPooling2dLayer([2 2],"Name","Encoder-Stage-1-MaxPool","Stride",[4 4])
    convolution2dLayer([4 4],128,"Name","Encoder-Stage-2-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Encoder-Stage-2-ReLU-1")
    convolution2dLayer([4 4],128,"Name","Encoder-Stage-2-Conv-2","Padding","same","WeightsInitializer","he")
    
    reluLayer("Name","Encoder-Stage-2-ReLU-2")];
lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
    maxPooling2dLayer([2 2],"Name","Encoder-Stage-2-MaxPool","Stride",[4 4])
    convolution2dLayer([4 4],256,"Name","Encoder-Stage-3-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Encoder-Stage-3-ReLU-1")
    convolution2dLayer([4 4],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")
    maxPooling2dLayer([2 2],"Name","Encoder-Stage-3-MaxPool","Stride",[4 4])
    convolution2dLayer([4 4],512,"Name","Bridge-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Bridge-ReLU-1")
    convolution2dLayer([4 4],512,"Name","Bridge-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Bridge-ReLU-2")
    dropoutLayer(0.5,"Name","Bridge-DropOut")
    transposedConv2dLayer([2 2],256,"Name","Decoder-Stage-1-UpConv","BiasLearnRateFactor",2,"Stride",[4 4],"WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-1-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
    depthConcatenationLayer(2,"Name","Decoder-Stage-1-DepthConcatenation")
    convolution2dLayer([4 4],256,"Name","Decoder-Stage-1-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-1-ReLU-1")
    convolution2dLayer([4 4],256,"Name","Decoder-Stage-1-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-1-ReLU-2")
    transposedConv2dLayer([2 2],128,"Name","Decoder-Stage-2-UpConv","BiasLearnRateFactor",2,"Stride",[4 4],"WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-2-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
    depthConcatenationLayer(2,"Name","Decoder-Stage-2-DepthConcatenation")
    convolution2dLayer([4 4],128,"Name","Decoder-Stage-2-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-2-ReLU-1")
    convolution2dLayer([4 4],128,"Name","Decoder-Stage-2-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-2-ReLU-2")
    transposedConv2dLayer([2 2],64,"Name","Decoder-Stage-3-UpConv","BiasLearnRateFactor",2,"Stride",[4 4],"WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-3-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
    depthConcatenationLayer(2,"Name","Decoder-Stage-3-DepthConcatenation")
    convolution2dLayer([4 4],64,"Name","Decoder-Stage-3-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-3-ReLU-1")
    convolution2dLayer([4 4],64,"Name","Decoder-Stage-3-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-3-ReLU-2")
    convolution2dLayer([1 1],3,"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");

% lgraph = connectLayers(lgraph,'relu12','skipConv1');
% lgraph = connectLayers(lgraph,'Encoder-Stage-2-Conv-2','add22/in2');
% lgraph = connectLayers(lgraph,'relu22','');
% Plot Layers
figure,plot(lgraph);

imageSize = [256 256 1];
numClasses = 2;
encoderDepth = 3;
lgraph = unetLayers(imageSize,numClasses,'EncoderDepth',encoderDepth)

options1 = trainingOptions('adam', ...
    'InitialLearnRate',1e-3, ...
    'MaxEpochs',100, ...
    'LearnRateDropFactor',5e-1, ...
    'LearnRateDropPeriod',10, ...
    'LearnRateSchedule','piecewise', ...
    'MiniBatchSize',4,'Plots','training-progress');

net1 = trainNetwork(ds,lgraph,options1);

[yanqi liu]

%% first, read the image data and labelled images
clc
clear all
dataSetDir = fullfile('C:\Users\Akmal\Desktop\I-131 256 28.02.2020\I-131 SPECT NEMA VALIDATION 01112019 256X256 26.09.2021 petang');
imageDir = fullfile(dataSetDir,'Image');
labelDir = fullfile(dataSetDir,'PixelLabelData');

imds = imageDatastore(imageDir);

% view data set images origional
figure
for i = 1:23
    subplot(5,5,i)
    I = readimage(imds,i);
    imshow(I)
    title('training labels')
end


%% train the data. if network already, then just drag it into command window
classNames = ["foreground" "background"];
labelIDs = [1 2];
pxds = pixelLabelDatastore(labelDir, classNames, labelIDs);

imds1 = imageDatastore(labelDir);
% figure
% for i = 1:5
%     subplot(3,3,i)
%     I = readimage(imds1,i);
%     imshow(I)
%     title('training labels')
% end

ds = pixelLabelImageDatastore(imds,pxds);

tbl = countEachLabel(pxds)

totalNumberOfPixels = sum(tbl.PixelCount);
frequency = tbl.PixelCount / totalNumberOfPixels;
inverseFrequency = 1./frequency

%    layerf = pixelClassificationLayer(...
%       'Classes',tbl.Name,'ClassWeights',inverseFrequency)
%   
layerf=pixelClassificationLayer("Name","Segmentation-Layer")

lgraph = layerGraph();

tempLayers = [
    
imageInputLayer([512/2 512/2 1],"Name","ImageInputLayer")

convolution2dLayer([4 4],64,"Name","Encoder-Stage-1-Conv-1","Padding","same","WeightsInitializer","he")
reluLayer("Name","Encoder-Stage-1-ReLU-1")
convolution2dLayer([4 4],64,"Name","Encoder-Stage-1-Conv-2","Padding","same","WeightsInitializer","he")
reluLayer("Name","Encoder-Stage-1-ReLU-2")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
    maxPooling2dLayer([2 2],"Name","Encoder-Stage-1-MaxPool","Stride",[4 4])
    convolution2dLayer([4 4],128,"Name","Encoder-Stage-2-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Encoder-Stage-2-ReLU-1")
    convolution2dLayer([4 4],128,"Name","Encoder-Stage-2-Conv-2","Padding","same","WeightsInitializer","he")
    
    reluLayer("Name","Encoder-Stage-2-ReLU-2")];
lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
    maxPooling2dLayer([2 2],"Name","Encoder-Stage-2-MaxPool","Stride",[4 4])
    convolution2dLayer([4 4],256,"Name","Encoder-Stage-3-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Encoder-Stage-3-ReLU-1")
    convolution2dLayer([4 4],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")
    maxPooling2dLayer([2 2],"Name","Encoder-Stage-3-MaxPool","Stride",[4 4])
    convolution2dLayer([4 4],512,"Name","Bridge-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Bridge-ReLU-1")
    convolution2dLayer([4 4],512,"Name","Bridge-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Bridge-ReLU-2")
    dropoutLayer(0.5,"Name","Bridge-DropOut")
    transposedConv2dLayer([2 2],256,"Name","Decoder-Stage-1-UpConv","BiasLearnRateFactor",2,"Stride",[4 4],"WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-1-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
    depthConcatenationLayer(2,"Name","Decoder-Stage-1-DepthConcatenation")
    convolution2dLayer([4 4],256,"Name","Decoder-Stage-1-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-1-ReLU-1")
    convolution2dLayer([4 4],256,"Name","Decoder-Stage-1-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-1-ReLU-2")
    transposedConv2dLayer([2 2],128,"Name","Decoder-Stage-2-UpConv","BiasLearnRateFactor",2,"Stride",[4 4],"WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-2-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
    depthConcatenationLayer(2,"Name","Decoder-Stage-2-DepthConcatenation")
    convolution2dLayer([4 4],128,"Name","Decoder-Stage-2-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-2-ReLU-1")
    convolution2dLayer([4 4],128,"Name","Decoder-Stage-2-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-2-ReLU-2")
    transposedConv2dLayer([2 2],64,"Name","Decoder-Stage-3-UpConv","BiasLearnRateFactor",2,"Stride",[4 4],"WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-3-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
    depthConcatenationLayer(2,"Name","Decoder-Stage-3-DepthConcatenation")
    convolution2dLayer([4 4],64,"Name","Decoder-Stage-3-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-3-ReLU-1")
    convolution2dLayer([4 4],64,"Name","Decoder-Stage-3-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-3-ReLU-2")
    convolution2dLayer([1 1],3,"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");

% lgraph = connectLayers(lgraph,'relu12','skipConv1');
% lgraph = connectLayers(lgraph,'Encoder-Stage-2-Conv-2','add22/in2');
% lgraph = connectLayers(lgraph,'relu22','');
% Plot Layers
figure,plot(lgraph);

imageSize = [256 256 1];
numClasses = 2;
encoderDepth = 3;
lgraph = unetLayers(imageSize,numClasses,'EncoderDepth',encoderDepth)

% split data
[imdsTrain, imdsVal, imdsTest, pxdsTrain, pxdsVal, pxdsTest] = partitionCamVidData(imds,pxds);
pximds = pixelLabelImageDatastore(imdsTrain,pxdsTrain, ...
    'DataAugmentation',augmenter);
pximdsVal = pixelLabelImageDatastore(imdsVal,pxdsVal);

options1 = trainingOptions('adam', ...
    'InitialLearnRate',1e-3, ...
    'MaxEpochs',100, ...
    'LearnRateDropFactor',5e-1, ...
    'LearnRateDropPeriod',10, ...
    'ValidationData',pximdsVal,...
    'LearnRateSchedule','piecewise', ...
    'MiniBatchSize',4,'Plots','training-progress');


net1 = trainNetwork(pximds,lgraph,options1);

[mohd akmal masud]

'partitionCamVidData' is used in Train and Deploy Fully Convolutional Networks for
Semantic Segmentation.

Got error

What is the meaning of this sir? Is it need to install?

[mohd akmal masud]

I haved put this partionCamVidData.m as attached in same folder. But got error below

Error using partitionCamVidData
Too many output arguments.

[yanqi liu]

yes，sir，can you upload zip file “I-131 SPECT NEMA VALIDATION 01112019 256X256 26.09.2021 petang”，so we can debug

[mohd akmal masud]

Sir, here I attached the imageDatastore (1) and pixelLabelDatastore (2)

[yanqi liu]

%% first, read the image data and labelled images
clc
clear all; close all;
dataSetDir = fullfile('C:\Users\Akmal\Desktop\I-131 256 28.02.2020\I-131 SPECT NEMA VALIDATION 01112019 256X256 26.09.2021 petang');
imageDir = fullfile(dataSetDir,'Image');
labelDir = fullfile(dataSetDir,'PixelLabelData');

imds = imageDatastore(imageDir);

% view data set images origional
% figure
% for i = 1:23
%     subplot(5,5,i)
%     I = readimage(imds,i);
%     imshow(I)
%     title('training labels')
% end


%% train the data. if network already, then just drag it into command window
classNames = ["foreground" "background"];
labelIDs = [1 2];
pxds = pixelLabelDatastore(labelDir, classNames, labelIDs);

imds1 = imageDatastore(labelDir);
% figure
% for i = 1:5
%     subplot(3,3,i)
%     I = readimage(imds1,i);
%     imshow(I)
%     title('training labels')
% end

ds = pixelLabelImageDatastore(imds,pxds);

tbl = countEachLabel(pxds)

totalNumberOfPixels = sum(tbl.PixelCount);
frequency = tbl.PixelCount / totalNumberOfPixels;
inverseFrequency = 1./frequency

%    layerf = pixelClassificationLayer(...
%       'Classes',tbl.Name,'ClassWeights',inverseFrequency)
%
layerf=pixelClassificationLayer("Name","Segmentation-Layer")

lgraph = layerGraph();

tempLayers = [
    
imageInputLayer([512/2 512/2 1],"Name","ImageInputLayer")

convolution2dLayer([4 4],64,"Name","Encoder-Stage-1-Conv-1","Padding","same","WeightsInitializer","he")
reluLayer("Name","Encoder-Stage-1-ReLU-1")
convolution2dLayer([4 4],64,"Name","Encoder-Stage-1-Conv-2","Padding","same","WeightsInitializer","he")
reluLayer("Name","Encoder-Stage-1-ReLU-2")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
    maxPooling2dLayer([2 2],"Name","Encoder-Stage-1-MaxPool","Stride",[4 4])
    convolution2dLayer([4 4],128,"Name","Encoder-Stage-2-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Encoder-Stage-2-ReLU-1")
    convolution2dLayer([4 4],128,"Name","Encoder-Stage-2-Conv-2","Padding","same","WeightsInitializer","he")
    
    reluLayer("Name","Encoder-Stage-2-ReLU-2")];
lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
    maxPooling2dLayer([2 2],"Name","Encoder-Stage-2-MaxPool","Stride",[4 4])
    convolution2dLayer([4 4],256,"Name","Encoder-Stage-3-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Encoder-Stage-3-ReLU-1")
    convolution2dLayer([4 4],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")
    maxPooling2dLayer([2 2],"Name","Encoder-Stage-3-MaxPool","Stride",[4 4])
    convolution2dLayer([4 4],512,"Name","Bridge-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Bridge-ReLU-1")
    convolution2dLayer([4 4],512,"Name","Bridge-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Bridge-ReLU-2")
    dropoutLayer(0.5,"Name","Bridge-DropOut")
    transposedConv2dLayer([2 2],256,"Name","Decoder-Stage-1-UpConv","BiasLearnRateFactor",2,"Stride",[4 4],"WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-1-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
    depthConcatenationLayer(2,"Name","Decoder-Stage-1-DepthConcatenation")
    convolution2dLayer([4 4],256,"Name","Decoder-Stage-1-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-1-ReLU-1")
    convolution2dLayer([4 4],256,"Name","Decoder-Stage-1-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-1-ReLU-2")
    transposedConv2dLayer([2 2],128,"Name","Decoder-Stage-2-UpConv","BiasLearnRateFactor",2,"Stride",[4 4],"WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-2-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
    depthConcatenationLayer(2,"Name","Decoder-Stage-2-DepthConcatenation")
    convolution2dLayer([4 4],128,"Name","Decoder-Stage-2-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-2-ReLU-1")
    convolution2dLayer([4 4],128,"Name","Decoder-Stage-2-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-2-ReLU-2")
    transposedConv2dLayer([2 2],64,"Name","Decoder-Stage-3-UpConv","BiasLearnRateFactor",2,"Stride",[4 4],"WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-3-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);

tempLayers = [
    depthConcatenationLayer(2,"Name","Decoder-Stage-3-DepthConcatenation")
    convolution2dLayer([4 4],64,"Name","Decoder-Stage-3-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-3-ReLU-1")
    convolution2dLayer([4 4],64,"Name","Decoder-Stage-3-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-3-ReLU-2")
    convolution2dLayer([1 1],3,"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");

% lgraph = connectLayers(lgraph,'relu12','skipConv1');
% lgraph = connectLayers(lgraph,'Encoder-Stage-2-Conv-2','add22/in2');
% lgraph = connectLayers(lgraph,'relu22','');
% Plot Layers
figure,plot(lgraph);

imageSize = [256 256 1];
numClasses = 2;
encoderDepth = 3;
lgraph = unetLayers(imageSize,numClasses,'EncoderDepth',encoderDepth)

% split data
[imdsTrain, imdsVal, pxdsTrain, pxdsVal] = partitionCamVidData2(imds,pxds);
pximds = pixelLabelImageDatastore(imdsTrain,pxdsTrain);
pximdsVal = pixelLabelImageDatastore(imdsVal,pxdsVal);
options1 = trainingOptions('adam', ...
    'InitialLearnRate',1e-3, ...
    'MaxEpochs',100, ...
    'LearnRateDropFactor',5e-1, ...
    'LearnRateDropPeriod',10, ...
    'ValidationData',pximdsVal,...
    'ValidationFrequency',3, ...
    'LearnRateSchedule','piecewise', ...
    'MiniBatchSize',4,'Plots','training-progress');
net1 = trainNetwork(pximds,lgraph,options1);

function [imdsTrain, imdsTest, pxdsTrain, pxdsTest] = partitionCamVidData2(imds,pxds)
% Partition CamVid data by randomly selecting 60% of the data for training. The
% rest is used for testing.
    
% Set initial random state for example reproducibility.
rng(0); 
numFiles = numel(imds.Files);
shuffledIndices = randperm(numFiles);

% Use 60% of the images for training.
N = round(0.60 * numFiles);
trainingIdx = shuffledIndices(1:N);

% Use the rest for testing.
testIdx = shuffledIndices(N+1:end);

% Create image datastores for training and test.
trainingImages = imds.Files(trainingIdx);
testImages = imds.Files(testIdx);
imdsTrain = imageDatastore(trainingImages);
imdsTest = imageDatastore(testImages);

% Extract class and label IDs info.
classes = pxds.ClassNames;
labelIDs = 1:numel(pxds.ClassNames);

% Create pixel label datastores for training and test.
trainingLabels = pxds.Files(trainingIdx);
testLabels = pxds.Files(testIdx);
pxdsTrain = pixelLabelDatastore(trainingLabels, classes, labelIDs);
pxdsTest = pixelLabelDatastore(testLabels, classes, labelIDs);
end

[mohd akmal masud]

Hi sir. Thank you very much for coding validation.

The trained completed. But got some error

function [imdsTrain, imdsTest, pxdsTrain, pxdsTest] = partitionCamVidData2(imds,pxds)
↑
Error: Function definition are not supported in this context. Functions can only be
created as local or nested functions in code files.

Actually what is the meaning of this?

[mohd akmal masud]

this is my partitionCamVidData2.m function. as attached

[yanqi liu]

yes，you should save it in one m file，or save partitionCamVidData2 as partitionCamVidData2.m in same folder

here is one file，please just download and run

[mohd akmal masud]

Dear yanqi,

can help me how to Compare Ground Truth Against Network Prediction

%% first, read the image data and labelled images
clc
clear all; close all;
dataSetDir = fullfile('C:\Users\Akmal\Desktop\I-131 256 28.02.2020\I-131 SPECT NEMA VALIDATION 01112019 256X256 26.09.2021 petang');
imageDir = fullfile(dataSetDir,'Image');
labelDir = fullfile(dataSetDir,'PixelLabelData');
imds = imageDatastore(imageDir);
% view data set images origional
% figure
% for i = 1:23
%     subplot(5,5,i)
%     I = readimage(imds,i);
%     imshow(I)
%     title('training labels')
% end
%% train the data. if network already, then just drag it into command window
classNames = ["foreground" "background"];
labelIDs = [1 2];
pxds = pixelLabelDatastore(labelDir, classNames, labelIDs);
imds1 = imageDatastore(labelDir);
% figure
% for i = 1:5
%     subplot(3,3,i)
%     I = readimage(imds1,i);
%     imshow(I)
%     title('training labels')
% end
ds = pixelLabelImageDatastore(imds,pxds);
tbl = countEachLabel(pxds)
totalNumberOfPixels = sum(tbl.PixelCount);
frequency = tbl.PixelCount / totalNumberOfPixels;
inverseFrequency = 1./frequency
%    layerf = pixelClassificationLayer(...
%       'Classes',tbl.Name,'ClassWeights',inverseFrequency)
%
layerf=pixelClassificationLayer("Name","Segmentation-Layer")
lgraph = layerGraph();
tempLayers = [
    
imageInputLayer([512/2 512/2 1],"Name","ImageInputLayer")
convolution2dLayer([4 4],64,"Name","Encoder-Stage-1-Conv-1","Padding","same","WeightsInitializer","he")
reluLayer("Name","Encoder-Stage-1-ReLU-1")
convolution2dLayer([4 4],64,"Name","Encoder-Stage-1-Conv-2","Padding","same","WeightsInitializer","he")
reluLayer("Name","Encoder-Stage-1-ReLU-2")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    maxPooling2dLayer([2 2],"Name","Encoder-Stage-1-MaxPool","Stride",[4 4])
    convolution2dLayer([4 4],128,"Name","Encoder-Stage-2-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Encoder-Stage-2-ReLU-1")
    convolution2dLayer([4 4],128,"Name","Encoder-Stage-2-Conv-2","Padding","same","WeightsInitializer","he")
    
    reluLayer("Name","Encoder-Stage-2-ReLU-2")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    maxPooling2dLayer([2 2],"Name","Encoder-Stage-2-MaxPool","Stride",[4 4])
    convolution2dLayer([4 4],256,"Name","Encoder-Stage-3-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Encoder-Stage-3-ReLU-1")
    convolution2dLayer([4 4],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")
    maxPooling2dLayer([2 2],"Name","Encoder-Stage-3-MaxPool","Stride",[4 4])
    convolution2dLayer([4 4],512,"Name","Bridge-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Bridge-ReLU-1")
    convolution2dLayer([4 4],512,"Name","Bridge-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Bridge-ReLU-2")
    dropoutLayer(0.5,"Name","Bridge-DropOut")
    transposedConv2dLayer([2 2],256,"Name","Decoder-Stage-1-UpConv","BiasLearnRateFactor",2,"Stride",[4 4],"WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-1-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    depthConcatenationLayer(2,"Name","Decoder-Stage-1-DepthConcatenation")
    convolution2dLayer([4 4],256,"Name","Decoder-Stage-1-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-1-ReLU-1")
    convolution2dLayer([4 4],256,"Name","Decoder-Stage-1-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-1-ReLU-2")
    transposedConv2dLayer([2 2],128,"Name","Decoder-Stage-2-UpConv","BiasLearnRateFactor",2,"Stride",[4 4],"WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-2-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    depthConcatenationLayer(2,"Name","Decoder-Stage-2-DepthConcatenation")
    convolution2dLayer([4 4],128,"Name","Decoder-Stage-2-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-2-ReLU-1")
    convolution2dLayer([4 4],128,"Name","Decoder-Stage-2-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-2-ReLU-2")
    transposedConv2dLayer([2 2],64,"Name","Decoder-Stage-3-UpConv","BiasLearnRateFactor",2,"Stride",[4 4],"WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-3-UpReLU")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    depthConcatenationLayer(2,"Name","Decoder-Stage-3-DepthConcatenation")
    convolution2dLayer([4 4],64,"Name","Decoder-Stage-3-Conv-1","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-3-ReLU-1")
    convolution2dLayer([4 4],64,"Name","Decoder-Stage-3-Conv-2","Padding","same","WeightsInitializer","he")
    reluLayer("Name","Decoder-Stage-3-ReLU-2")
    convolution2dLayer([1 1],3,"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");
% lgraph = connectLayers(lgraph,'relu12','skipConv1');
% lgraph = connectLayers(lgraph,'Encoder-Stage-2-Conv-2','add22/in2');
% lgraph = connectLayers(lgraph,'relu22','');
% Plot Layers
figure,plot(lgraph);
imageSize = [256 256 1];
numClasses = 2;
encoderDepth = 3;
lgraph = unetLayers(imageSize,numClasses,'EncoderDepth',encoderDepth)
% split data
[imdsTrain, imdsVal, pxdsTrain, pxdsVal] = partitionCamVidData2(imds,pxds);
pximds = pixelLabelImageDatastore(imdsTrain,pxdsTrain);
pximdsVal = pixelLabelImageDatastore(imdsVal,pxdsVal);
options1 = trainingOptions('adam', ...
    'InitialLearnRate',1e-3, ...
    'MaxEpochs',100, ...
    'LearnRateDropFactor',5e-1, ...
    'LearnRateDropPeriod',10, ...
    'ValidationData',pximdsVal,...
    'ValidationFrequency',3, ...
    'LearnRateSchedule','piecewise', ...
    'MiniBatchSize',4,'Plots','training-progress');
net1 = trainNetwork(pximds,lgraph,options1);
function [imdsTrain, imdsTest, pxdsTrain, pxdsTest] = partitionCamVidData2(imds,pxds)
% Partition CamVid data by randomly selecting 60% of the data for training. The
% rest is used for testing.

% Set initial random state for example reproducibility.
rng(0); 
numFiles = numel(imds.Files);
shuffledIndices = randperm(numFiles);
% Use 60% of the images for training.
N = round(0.60 * numFiles);
trainingIdx = shuffledIndices(1:N);
% Use the rest for testing.
testIdx = shuffledIndices(N+1:end);
% Create image datastores for training and test.
trainingImages = imds.Files(trainingIdx);
testImages = imds.Files(testIdx);
imdsTrain = imageDatastore(trainingImages);
imdsTest = imageDatastore(testImages);
% Extract class and label IDs info.
classes = pxds.ClassNames;
labelIDs = 1:numel(pxds.ClassNames);
% Create pixel label datastores for training and test.
trainingLabels = pxds.Files(trainingIdx);
testLabels = pxds.Files(testIdx);
pxdsTrain = pixelLabelDatastore(trainingLabels, classes, labelIDs);
pxdsTest = pixelLabelDatastore(testLabels, classes, labelIDs);
end