Wolfram Language

Réseaux neuronaux

Classification d'objets

Utiliser a base de données ICRA-10 d'images étiquetées, pour entraîner un réseau de convolution pour prédire la classe de chaque objet.

Tout d'abord obtenez les données d'entraînement.

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

Extrayez les classes uniques.

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

Créez un réseau de convolution qui prédit la classe donnée d'une image.

In[3]:=
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lenet = NetChain[ {ConvolutionLayer[20, 5], Ramp, PoolingLayer[2, 2], ConvolutionLayer[50, 5], Ramp, PoolingLayer[2, 2], FlattenLayer[], 500, Ramp, 10, SoftmaxLayer[]}, "Output" -> NetDecoder[{"Class", classes}], "Input" -> NetEncoder[{"Image", {32, 32}}] ]
Out[3]=

Entraînez le réseau sur les données d'entraînement.

In[4]:=
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trained = NetTrain[lenet, trainingData, MaxTrainingRounds -> 4]
Out[5]=

Prédites les classes les plus probables pour un ensemble d'images.

In[6]:=
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Out[6]=

Pour un exemple précis, donnez les probabilités des assignations d'étiquette les plus probables.

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

Sur un échantillon aléatoire, sélectionnez les images pour lesquelles le réseau produit les plus élevées et les plus basses prédictions d'entropie. Les entrées d'entropie hautes peuvent être interprétées comme celles pour lesquelles le réseau est le plus incertain au sujet de la classe correcte.

In[8]:=
Click for copyable input
images = RandomSample[Keys[trainingData], 5000];
In[9]:=
Click for copyable input
entropies = trained[images, "Entropy"];
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Click for copyable input
Labeled[images[[Ordering[entropies, -10]]], "high entropy"] Labeled[images[[Ordering[entropies, 10]]], "low entropy"]
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Exemples connexes

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