Data augmentation in CNN

Helo... Iam working on a dataset of 300 images containing 2 classes. Below is the code i have attached for data augmentation in CNN.

But the error is that if am running the code for multiple times the accuracy is not at all constant, its changing and also the accuracy is decreasing for data augmentation. Could any one plz help me in solving this problem.

clc;
clear all;
close all;
myTrainingFolder = 'C:\Users\Admin\Desktop\Major Project\cnn_dataset';
%testingFolder = 'C:\Users\Be Happy\Documents\MATLAB\gtsrbtest';


imds = imageDatastore(myTrainingFolder,'IncludeSubfolders', true,  'LabelSource', 'foldernames');
%testingSet = imageDatastore(testingFolder,'IncludeSubfolders', true,  'LabelSource', 'foldernames');
labelCount = countEachLabel(imds);
numClasses = height(labelCount);
numImagesTraining = numel(imds.Files);
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
%This very small data set now contains 55 training images and 20 validation images. Display some sample images.

numTrainImages = numel(imdsTrain.Labels);
imageSize = [227 227 3];
inputSize = [227 227 3];
layer1 = [
    imageInputLayer(inputSize)
    convolution2dLayer(5,20)
    batchNormalizationLayer
    reluLayer
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];
layers = [
    imageInputLayer(imageSize)
    
    convolution2dLayer(3,8,'Padding','same')
    batchNormalizationLayer
    reluLayer   
    
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer   
    
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer   
    
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];
options1 = trainingOptions('sgdm', ...
    'InitialLearnRate',0.01, ...
    'MaxEpochs',15, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',3, ...
    'Verbose',false, ...
    'Plots','training-progress');
netTransfer1 = trainNetwork(imdsTrain,layers,options1);

YPred = classify(netTransfer1,imdsValidation);
YValidation = imdsValidation.Labels;
netTransfer1BaselineAccuracy = sum(YPred == YValidation)/numel(YValidation);

inputSize = [227 227 3];
imageAugmenter = imageDataAugmenter( ...
    'RandRotation',[-20,20], ...
    'RandXReflection',1,...
    'RandYReflection',1,...
    'RandXTranslation',[-3 3], ...
    'RandYTranslation',[-3 3]);
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain, ...
    'DataAugmentation',imageAugmenter);

augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation);


netTransfer = trainNetwork(augimdsTrain,layers,options1);

%Classify Validation Images

[YPred,scores] = classify(netTransfer,augimdsValidation);
%Display four sample validation images with their predicted labels.

idx = randperm(numel(imdsValidation.Files),4);
figure
for i = 1:4
    subplot(2,2,i)
    I = readimage(imdsValidation,idx(i));
    imshow(I)
    label = YPred(idx(i));
    title(string(label));
end
YValidation = imdsValidation.Labels;
accuracy = mean(YPred == YValidation);

Answer 1

[Raunak Gupta]

Hi,

I see that the amount of data that you have is quiet less, so the Data Augmentation is very much appropriate while training the network. The accuracy values that are mentioned in the question will always be different when you train from scratch because of random initialization of weights in first epoch. So here are the few things you may try,

• Try to check the accuracy without using data agumentation, it might be possible that data augmentation is making the model more generalized and hence will have less testing error.
• Try to train for more epochs so that model settles down to a lower value of loss and you may conclude that the training is completed.

You may refer to Deep Learning Tips and Tricks for more details.

[Srinidhi Gorityala]

thanks a lot.