Aprendizaje de múltiples tareas
Entrene una red convolucional para que clasifique tanto las etiquetas como las subetiquetas de imágenes en un conjunto de datos CIFAR-100.
Obtenga el conjunto de datos de entrenamiento.
In[1]:=
obj = ResourceObject["CIFAR-100"];
trainingData = ResourceData[obj, "TrainingDataset"];
RandomSample[trainingData, 5]
Out[1]=
Obtenga las etiquetas y subetiquetas de las imágenes.
In[2]:=
labels = Union@Normal@trainingData[All, "Label"]
sublabels = Union@Normal@trainingData[All, "SubLabel"]
Out[2]=
Out[2]=
Defina una simple red convolucional.
In[3]:=
convnet = NetChain[{
ConvolutionLayer[20, {5, 5}],
ElementwiseLayer[Ramp],
PoolingLayer[{2, 2}, {2, 2}],
ConvolutionLayer[50, {5, 5}],
ElementwiseLayer[Ramp],
PoolingLayer[{2, 2}, {2, 2}],
FlattenLayer[],
DotPlusLayer[500],
ElementwiseLayer[Ramp]
}, "Input" -> NetEncoder[{"Image", {32, 32}}]]
Out[3]=
Cree una red que utilice el resultado de la red convolucional para crear predicciones de etiqueta y subetiquetas.
In[4]:=
net = NetGraph[{convnet, 100, SoftmaxLayer[], 20,
SoftmaxLayer[]}, {NetPort["Image"] ->
1 -> 2 -> 3 -> NetPort["SubLabel"],
2 -> 4 -> 5 -> NetPort["Label"]},
"Label" -> NetDecoder[{"Class", labels}],
"SubLabel" -> NetDecoder[{"Class", sublabels}]]
Out[4]=
Entrene la red, permitiendo que NetTrain infiera automáticamente que debería adjuntar funciones de pérdida de entropía cruzada a ambas salidas.
In[5]:=
net = NetTrain[net, trainingData]
Out[6]=
Clasifique una imagen y obtenga tanto la etiqueta como la subetiqueta.
In[7]:=
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Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{32, 32},
PlotRange->{{0, 32}, {0, 32}}]\)]
Out[7]=
Obtenga las clasificaciones más probables para la salida "SubLabel" o "Subetiqueta".
In[8]:=
net[\!\(\*
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Out[8]=