Wolfram Language

Redes neurais

Acelere o treinamento usando um GPU

Acelere o treinamento de uma rede de reconhecimento de objetos usando um GPU NVIDIA.

Primeiro obtenha os dados de treinamento.

In[1]:=
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obj = ResourceObject["CIFAR-10"]; trainingData = ResourceData[obj, "TrainingData"]; RandomSample[trainingData, 5]
Out[1]=

Extraia o conjunto de classes.

In[2]:=
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classes = Union@Values[trainingData]
Out[2]=

Construa uma rede de alta precisão usando módulos repetidos.

In[3]:=
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module = NetChain[{ ConvolutionLayer[100, {3, 3}], BatchNormalizationLayer[], ElementwiseLayer[Ramp], PoolingLayer[{3, 3}, "PaddingSize" -> 1] }]
Out[3]=
In[4]:=
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net = NetChain[{ module, module, module, module, FlattenLayer[], 500, Ramp, 10, SoftmaxLayer[]}, "Input" -> NetEncoder[{"Image", {32, 32}}], "Output" -> NetDecoder[{"Class", classes}] ]
Out[4]=

Treine a rede e registre o tempo gasto.

In[5]:=
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{time, trained} = AbsoluteTiming @ NetTrain[net, trainingData, TargetDevice -> "GPU"];

Treinar em um GPU Titan X da NVidia leva cerca de 10 minutos.

In[6]:=
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time
Out[6]=

Para comprara, o treinamento em CPU pode levar mais de 2 horas.

Execute a rede em uma seleção de imagens.

In[7]:=
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Out[7]=

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