The CNN model only predicts a single class out of three?
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Vinay Chawla
am 14 Mai 2020
Beantwortet: Vinay Chawla
am 24 Jul. 2020
Hello,
I am working on training a CNN model for pavement distress identification using image classification. I have three categories for defects (Alligator Cracks, Potholes and Rutting). When I run my model it only predicts a single class out of three rendering my model accuracy to 33.33%. I tried changing the training parameters/ options but the result is same. I was hoping if someone can help me figure out why. The code I am using is given below. Thanks in advance.
_______________________________________________________________________________________________________________________________________
rootFolder = fullfile ('CNN Input');
categories = {'Alligator Cracks','Potholes','Rutting'};
imds = imageDatastore (fullfile (rootFolder,categories), 'LabelSource', 'Foldernames');
tbl = countEachLabel (imds);
minSetCount = min(tbl{:,2});
countEachLabel(imds);
AlligatorCracks = find(imds.Labels == 'Alligator Cracks',1);
Potholes = find(imds.Labels == 'Potholes',1);
Rutting = find(imds.Labels == 'Rutting',1);
net = alexnet ();
rng ('default');
net.Layers(1);
net.Layers(end);
numel(net.Layers(end).ClassNames);
[trainingSet, testSet, validationSet] = splitEachLabel(imds,0.75,0.15,'randomize');
imageSize = net.Layers(1).InputSize;
augmentedTrainingSet = augmentedImageDatastore(imageSize,trainingSet,'ColorPreprocessing','gray2rgb');
augmentedTestSet = augmentedImageDatastore(imageSize,testSet,'ColorPreprocessing','gray2rgb');
augmentedValidationSet = augmentedImageDatastore(imageSize,validationSet,'ColorPreprocessing','gray2rgb');
w1 = net.Layers(2).Weights;
w1 = mat2gray(w1);
featurelayer = 'drop7';
trainingFeatures = activations(net, augmentedTrainingSet, featurelayer,'MiniBatchSize', 32,'OutputAs', 'columns');
options = trainingOptions('sgdm', ...
'LearnRateSchedule','piecewise',...
'LearnRateDropFactor',0.2,...
'learnRateDropPeriod',1,...
'MaxEpochs',8,...
'MiniBatchSize',16,...
'Plots','training-progress',...
'Shuffle','once');
numClasses = 3;
layersTransfer = net.Layers(1:end-3);
layers = [
layersTransfer
fullyConnectedLayer(numClasses,'WeightLearnRateFactor',20,'BiasLearnRateFactor',20)
softmaxLayer
classificationLayer];
trainedNet = trainNetwork(augmentedTrainingSet,layers,options);
trainingLabels = trainingSet.Labels;
classifier = fitcecoc(trainingFeatures, trainingLabels, 'Learner', 'Linear', 'Coding', 'onevsall','observationsIn','columns');
testFeatures = activations(trainedNet,augmentedTestSet, featurelayer,'MiniBatchSize', 32,'OutputAs', 'columns');
validationFeatures = activations(trainedNet,augmentedValidationSet, featurelayer,'MiniBatchSize', 32,'OutputAs', 'columns');
predictLabels = predict(classifier,testFeatures,'ObservationsIn','columns');
predictLabelss = predict(classifier,validationFeatures,'ObservationsIn','columns');
testLables = testSet.Labels;
validationLables = validationSet.Labels;
confMat = confusionmat(testLables, predictLabels);
confMatt = confusionmat(validationLables, predictLabelss);
confMat = bsxfun(@rdivide, confMat, sum(confMat,2));
confMatt = bsxfun(@rdivide, confMatt, sum(confMatt,2));
mean(diag(confMat));
mean(diag(confMatt));
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Akzeptierte Antwort
Shishir Singhal
am 22 Mai 2020
Hi, You facing this problem may be because of class imbalance.
Just check the number of datapoints you have in each class.
To handle the class imbalance, you can use data augmentation techniques.
For data augmentation in MATLAB, you can refer to this link: https://www.mathworks.com/help/deeplearning/ref/imagedataaugmenter.html
3 Kommentare
Shishir Singhal
am 27 Mai 2020
Bearbeitet: Shishir Singhal
am 27 Mai 2020
Yes, do data augmentation multiple times.
You can refer to these links to know more about data augmentation techniques:
In your case, it also might happened an unusual split.
For example, it may happen that our training set consist more examples for a particular class. Please check the distribution of each class in train, test and validation set.
Moreover, first apply data augmentation technique in your data. And then do stratified split. Because, if there is an unsual split, and you are applying data augmentation on top of it, then you have almost similiar kind of images in your training set which makes your traning model bias towards a particular class.
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