Wolfram Language

Redes neuronales

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]:=
Click for copyable input
obj = ResourceObject["CIFAR-100"]; trainingData = ResourceData[obj, "TrainingDataset"]; RandomSample[trainingData, 5]
Out[1]=

Obtenga las etiquetas y subetiquetas de las imágenes.

In[2]:=
Click for copyable input
labels = Union@Normal@trainingData[All, "Label"] sublabels = Union@Normal@trainingData[All, "SubLabel"]
Out[2]=
Out[2]=

Defina una simple red convolucional.

In[3]:=
Click for copyable input
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]:=
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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]:=
Click for copyable input
net = NetTrain[net, trainingData]
Out[6]=

Clasifique una imagen y obtenga tanto la etiqueta como la subetiqueta.

In[7]:=
Click for copyable input
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Out[7]=

Obtenga las clasificaciones más probables para la salida "SubLabel" o "Subetiqueta".

In[8]:=
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Out[8]=

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