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]:=
obj = ResourceObject["CIFAR-10"];
trainingData = ResourceData[obj, "TrainingData"];
RandomSample[trainingData, 5]
Out[1]=
Extraia o conjunto de classes.
In[2]:=
classes = Union@Values[trainingData]
Out[2]=
Construa uma rede de alta precisão usando módulos repetidos.
In[3]:=
module = NetChain[{
ConvolutionLayer[100, {3, 3}],
BatchNormalizationLayer[],
ElementwiseLayer[Ramp],
PoolingLayer[{3, 3}, "PaddingSize" -> 1]
}]
Out[3]=
In[4]:=
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]:=
{time, trained} =
AbsoluteTiming @ NetTrain[net, trainingData, TargetDevice -> "GPU"];
Treinar em um GPU Titan X da NVidia leva cerca de 10 minutos.
In[6]:=
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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