Convolutional neural networks: What is the best practice training approach using graphics cards?
1 Ansicht (letzte 30 Tage)
Ältere Kommentare anzeigen
quincy
am 18 Okt. 2017
Kommentiert: quincy
am 28 Okt. 2017
Training a convolutional neural network (CNN) for image classification, I successfully used the trainNetwork function employing 4 CPU cores. However, the process takes quite a lot of time (hours) and must be accelerated, e.g by using a graphics card.
Currently, I pass a tbl to trainNetwork containing the image paths and labels. I suppose that images are read from the disk and then sequentially processed by the function. This might work for CPU based processing system to some extent. However, using a GPU, I assume that this approach will significantly slow down the training process due to a number of GPU accesses and related delays. Is it e.g. possible to transfer the training data batch-wise to the graphics card or is this automatically done using the parallel processing toolbox? How do I have to adapt my code in this case? It would be great to have a minimalistic code snippet.
Thank you! Best, Stephan
P.S.: I should mention that I cannot use an imageDatastore, since this datatype apparently does not work for regression CNNs which I use.
0 Kommentare
Akzeptierte Antwort
Joss Knight
am 27 Okt. 2017
Bearbeitet: Joss Knight
am 27 Okt. 2017
You needn't worry too much about the efficiency of file i/o. Even with a file-path table, data is prefetched in the background during training. Your only concern is setting the mini-batch size appropriately for maximum efficiency.
Weitere Antworten (1)
Corey Silva
am 24 Okt. 2017
You can use the "trainingOptions" function to tell the "trainNetwork" function to use the GPU.
For example, if we already have "trainDigitData" and "layers" defined, then the following example does this:
>> options = trainingOptions('sgdm','ExecutionEnvironment','gpu');
>> convnet = trainNetwork(trainDigitData,layers,options);
1 Kommentar
Siehe auch
Kategorien
Mehr zu Image Data Workflows finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!