Load of Transfer Learning on GPU

Question

Johnny Yeng 2019-2-16

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/445299-load-of-transfer-learning-on-gpu

回答： Joss Knight 2019-2-16

Hi Guys,

I've been trying the transfer learning examples provided by mathworks with my own datasete (105 different labels) on the pretrained models matlab provided.

When training, I noticed that the Training Progress window dows say 'Hardware Resourse : Single GPU' and I've seen the 'Cuda library needs to be recompiled...' warning which I guess that indicates matlab does see a gpu existence.

My problem is while transfer learning using alexnet or currently training using vgg-16

takes 3 hours and beyond (Alexnet spent 4 hours)
GPU load in task manager shows 5% maximum and sometimes even 0% (CPU load is around 20%)...

Is this normal for a single 2080Ti card or is it not trained on the gpu for some reasons?

Thanks in advance !

-----------------------------------------------------------------------

imds = imageDatastore('Train_single','IncludeSubfolders',true,'LabelSource','foldernames');
inputSize = [224 224];
imds.ReadFcn = @(loc)imresize(imread(loc),inputSize);
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
net = vgg16();
layersTransfer = net.Layers(1:end-3);
numClasses = numel(categories(imdsTrain.Labels));
layers = [
    layersTransfer
    fullyConnectedLayer(numClasses,'WeightLearnRateFactor',10,'BiasLearnRateFactor',10)
    softmaxLayer
    classificationLayer];
options = trainingOptions('sgdm', ...
    'MiniBatchSize',10, ...
    'MaxEpochs',6, ...
    'InitialLearnRate',1e-4, ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',3, ...
    'Verbose',false, ...
    'Plots','training-progress');
netTransfer = trainNetwork(imdsTrain,layers,options'Hㄍ);
YPred = classify(netTransfer,imdsValidation);
accuracy = mean(YPred == imdsValidation.Labels);

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Answer 1

Joss Knight 2019-2-16

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/445299-load-of-transfer-learning-on-gpu#answer_361349

Your problem is this line:

imds.ReadFcn = @(loc)imresize(imread(loc),inputSize);

You should remove it and instead use an augmentedImageDatastore

imds = imageDatastore('Train_single','IncludeSubfolders',true,'LabelSource','foldernames');
inputSize = [224 224];
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
augdsTrain = augmentedImageDatastore(inputSize, imdsTrain);
augdsValidation = augmentedImageDatastore(inputSize, imdsValidation);
net = vgg16();
layersTransfer = net.Layers(1:end-3);
numClasses = numel(categories(imdsTrain.Labels));
layers = [
    layersTransfer
    fullyConnectedLayer(numClasses,'WeightLearnRateFactor',10,'BiasLearnRateFactor',10)
    softmaxLayer
    classificationLayer];
options = trainingOptions('sgdm', ...
    'MiniBatchSize',10, ...
    'MaxEpochs',6, ...
    'InitialLearnRate',1e-4, ...
    'ValidationData',augdsValidation, ...
    'ValidationFrequency',3, ...
    'Verbose',false, ...
    'Plots','training-progress');
netTransfer = trainNetwork(augdsTrain,layers,options);
YPred = classify(netTransfer,augdsValidation);
accuracy = mean(YPred == imdsValidation.Labels);