How to train a network that has multi-classes image classification

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Mohamed Elbeialy
Mohamed Elbeialy on 3 Apr 2020
Edited: Mohammad Sami on 8 Apr 2020
I have formulated a code to train an image datastore netwrok that has 5 number of classes, however the valdiation, testing, and training have done on one class only, evernthough there are 5 classes on the network. How to force network train, test and validate the 5 classes and give an equal result between classes
Note: all classes have same number of images
imds = imageDatastore('D:\dataimage','IncludeSubfolders',true,'LabelSource','foldernames');
[imdsTrain,imdsValidation,imdsTest] = splitEachLabel(imds,0.8,0.1,0.1);
augimdsTrain = augmentedImageDatastore([227 227],imdsTrain); % resize images
augimdsValidation = augmentedImageDatastore([227 227],imdsValidation); % resize image
augimdsTest = augmentedImageDatastore([227 227],imdsTest);
numClasses = numel(categories(imdsTrain.Labels));
T = countEachLabel(imds); % count number of images in folder
y=ceil((T.Count*10/100)); % to count 10% of images integer number
sum (y); % for sum folders number (note) if other sum varible use, it will give error. Use 'clear sum'
z=ceil((y)+(T.Count* 40/100)); % 50% of whole images
m=ceil((z)+(T.Count*10/100)); % 60% % ceil: round the fraction number to nearest high integer
w=ceil((m)+(T.Count* 40/100)); % 90 %
valset1=subset(imds,(1:(y-1)));
t2=countEachLabel(valset1);
trainset1=subset(imds,((y):(y+z-1)));
t1=countEachLabel(trainset1); % # for training part
testset1=subset(imds,((z):(z+m-1))); % floor: round the fraction number to nearest small inger
t3=countEachLabel(testset1);
trainset2=subset(imds,((m):(m+w-1)));
t4=countEachLabel(trainset2);
  3 Comments
Mohammad Sami
Mohammad Sami on 8 Apr 2020
If you want to split two separate training sets of 40% each just modify the splitEachLabel call as follows
[imdsTrain1,imdsTrain2,imdsValidation,imdsTest] = splitEachLabel(imds,0.4,0.4,0.1,0.1);

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Answers (1)

Shishir Singhal
Shishir Singhal on 7 Apr 2020
May be you are facing the problem of unsual split.
You can also do like:
  1. split your data into 5 subsets each belongs to one class.
  2. Then from each subset take random train(40%, 40%), valid(10%), and test(10%) datapoints.
  3. With this way, your train, test and validation set will definetly contains sample for all 5 classes.
Now you can train your model. But make sure that your classses should be balaced otherwise your model will get biased and does not give good results.

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