Out of memory while training

layers = [ ...
    imageInputLayer([768 1024 3])
    convolution2dLayer(5,20)
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    fullyConnectedLayer(2)
    softmaxLayer
    classificationLayer];

options = trainingOptions('sgdm', ...
    'MaxEpochs',20,...
    'InitialLearnRate',1e-4, ...
    'Verbose',false, ...
    'Plots','training-progress');

imds = imageDatastore('datasets/train', ...
    'IncludeSubfolders',true, ...
    'LabelSource','foldernames');

inputSize = [768 1024];
imds.ReadFcn = @(loc)imresize(imread(loc),inputSize);

numTrainingFiles = 51;
[imdsTrain,imdsTest] = splitEachLabel(imds,numTrainingFiles,'randomize');

net = trainNetwork(imdsTrain,layers,options);

save('net.mat','net');

I have the code above train a model using set of images. I give file location "train" and it contains two subfile which include training images. Problem is that when I start training, it gives out of memory error and it just has 100 image in it. When I train one subfile only, it does it flawlessly. Error may happen because of the following line.

inputSize = [768 1024];
imds.ReadFcn = @(loc)imresize(imread(loc),inputSize);

Answer 1

[Gaurav Garg]

Hi,

Consider using ‘MiniBatchSize’ and 'ExecutionEnvironment' as arguments in trainingOptions.

Using ‘MiniBatchSize’, you can specify the size of mini-batch you wish to use for training. You can try using size as 10 in your case.

Using 'ExecutionEnvironment', you can set the environment to CPU or GPU or auto (to train on a GPU, if available).

You can go through the documentation as well for more information on the trainingOptions function.