how to validate data trainned

1 visualización (últimos 30 días)
mohd akmal masud
mohd akmal masud el 7 de En. de 2022
Comentada: mohd akmal masud el 29 de Mayo de 2022
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??

Respuesta aceptada

yanqi liu
yanqi liu el 8 de En. de 2022
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');
  11 comentarios
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

Iniciar sesión para comentar.

Más respuestas (0)

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by