Neural Networks

Accelerate Training Using a GPU

Accelerate the training of an object recognition network using an NVIDIA GPU.

First obtain the training data.

In[1]:=
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obj = ResourceObject["CIFAR-10"]; trainingData = ResourceData[obj, "TrainingData"]; RandomSample[trainingData, 5]
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Extract the set of classes.

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classes = Union@Values[trainingData]
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Construct a high-accuracy net using repeated modules.

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module = NetChain[{ ConvolutionLayer[100, {3, 3}], BatchNormalizationLayer[], ElementwiseLayer[Ramp], PoolingLayer[{3, 3}, "PaddingSize" -> 1] }]
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net = NetChain[{ module, module, module, module, FlattenLayer[], 500, Ramp, 10, SoftmaxLayer[]}, "Input" -> NetEncoder[{"Image", {32, 32}}], "Output" -> NetDecoder[{"Class", classes}] ]
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Train the network and record the time taken.

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{time, trained} = AbsoluteTiming @ NetTrain[net, trainingData, TargetDevice -> "GPU"];

Training on an NVidia Titan X GPU takes around 10 minutes.

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time
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For comparison, CPU training can take upward of 2 hours.

Evaluate the network on a selection of images.

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Out[7]=

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