Wolfram Language

Redes neuronales

Acelere el entrenamiento usando un GPU

Acelere el entrenamiento de una red de reconocimiento de objetos usando un GPU de NVIDIA.

Primero obtenga los datos de entrenamiento.

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

Extraiga el conjunto de clases.

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

Construya una red de alta precisión 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]=

Entrene la red y registre el tiempo que toma.

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

El entrenamiento en un Titan X GPU de NVidia toma alrededor de 10 minutos.

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

Para comparar, el entrenamiento en CPU puede tomar hasta 2 horas.

Evalúe la red en una selección de imágenes.

In[7]:=
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Out[7]=

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