0 votos

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??

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yanqi liu
yanqi liu el 8 de En. de 2022

0 votos

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');

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mohd akmal masud
mohd akmal masud el 9 de En. de 2022
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
yanqi liu el 10 de En. de 2022
%% 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
mohd akmal masud el 10 de En. de 2022
'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
mohd akmal masud el 10 de En. de 2022
I haved put this partionCamVidData.m as attached in same folder. But got error below
Error using partitionCamVidData
Too many output arguments.
yanqi liu
yanqi liu el 10 de En. de 2022
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
mohd akmal masud el 10 de En. de 2022
Sir, here I attached the imageDatastore (1) and pixelLabelDatastore (2)
yanqi liu
yanqi liu el 11 de En. de 2022
%% 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
mohd akmal masud el 11 de En. de 2022
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
mohd akmal masud el 11 de En. de 2022
this is my partitionCamVidData2.m function. as attached
yanqi liu
yanqi liu el 11 de En. de 2022
Editada: yanqi liu el 11 de En. de 2022
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
mohd akmal masud el 29 de Mayo de 2022
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

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R2021b

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Preguntada:

mohd akmal masud
mohd akmal masud
el 7 de En. de 2022

Comentada:

mohd akmal masud
mohd akmal masud
el 29 de Mayo de 2022

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