Wolfram Language

Réseaux neuronaux

Apprentissage multitâche

Entraînez un réseau de convolution pour classer les deux étiquettes et sous étiquettes des images dans l'ensemble de données ICRA-100.

Obtenez l'ensemble de données de formation.

In[1]:=
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obj = ResourceObject["CIFAR-100"]; trainingData = ResourceData[obj, "TrainingDataset"]; RandomSample[trainingData, 5]
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Obtenez les étiquettes et sous étiquettes des images.

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

Définissez un réseau de convolution simple.

In[3]:=
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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}}]]
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Créez un réseau qui utilise le résultat du réseau convolution pour faire des prédictions d'étiquettes et sous-étiquettes.

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}]]
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Entraînez le réseau, permettant que NetTrain déduise automatiquement qu'il doit joindre les fonctions de perte d'entropie croisée aux deux sorties.

In[5]:=
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net = NetTrain[net, trainingData]
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Classifiez une image et obtenez à la fois l'étiquette et la sous-étiquête.

In[7]:=
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Out[7]=

Obtenez les classifications les plus probables pour la sortie "sous-étiquête".

In[8]:=
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Out[8]=

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