Wolfram Language

Neuronale Netze

Training durch einen Grafikprozessor beschleunigen

Beschleunigen Sie das Training eines Netzes zur Objekterkennung durch einen NVIDIA-Grafikprozessor.

Erstellen Sie als ersten Schritt Trainingsdaten.

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

Extrahieren Sie die Klassen.

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

Konstruieren Sie ein sehr genaues Netz durch wiederholte Module.

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]=

Trainieren Sie das Netzwerk und zeichnen Sie die dafür benötigte Zeit auf.

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

Das Trainieren eines Netzes mit einem NVidia Titan X-Grafikprozessor dauert etwa 10 Minuten.

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

Zum Vergleich: das Training mit einem herkömmlichen Prozessor kann über 2 Stunden dauern.

Testen Sie die Güte des Netzes anhand einer Auswahl an Bildern.

In[7]:=
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Out[7]=

Verwandte Beispiele

en es fr ja ko pt-br ru zh