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]:=
obj = ResourceObject["CIFAR-10"];
trainingData = ResourceData[obj, "TrainingData"];
RandomSample[trainingData, 5]
Out[1]=
Extrahieren Sie die Klassen.
In[2]:=
classes = Union@Values[trainingData]
Out[2]=
Konstruieren Sie ein sehr genaues Netz durch wiederholte Module.
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]=
Trainieren Sie das Netzwerk und zeichnen Sie die dafür benötigte Zeit auf.
In[5]:=
{time, trained} =
AbsoluteTiming @ NetTrain[net, trainingData, TargetDevice -> "GPU"];
Das Trainieren eines Netzes mit einem NVidia Titan X-Grafikprozessor dauert etwa 10 Minuten.
In[6]:=
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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