Classification d'objets
Utilisez la base de données ICRA-10 d'images étiquetées pour entraîner un réseau de convolution afin de prédire la classe de chaque objet.
Pour commencer, obtenez les données d'apprentissage.
obj = ResourceObject["CIFAR-10"];
trainingData = ResourceData[obj, "TrainingData"];
RandomSample[trainingData, 5]
Extrayez les classes uniques.
classes = Union@Values[trainingData]
Créez un réseau de convolution qui prédit la classe donnée d'une image.
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}}]
]
Entraînez le réseau sur les données d'apprentissage.
trained = NetTrain[lenet, trainingData, MaxTrainingRounds -> 4]
Prédisez les classes les plus probables pour un ensemble d'images.
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Pour un exemple précis, donnez les probabilités des assignations d'étiquette les plus probables.
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"], {{0, 32}, {32, 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{32, 32},
PlotRange->{{0, 32}, {0, 32}}]\), {"TopProbabilities", 3}]
Sur un échantillon aléatoire, sélectionnez les images pour lesquelles le réseau produit les prédictions d'entropie les plus élevées et les plus basses. Les entrées d'entropie élevées peuvent être interprétées comme celles pour lesquelles le réseau est le plus incertain au sujet de la classe correcte.
images = RandomSample[Keys[trainingData], 5000];
entropies = trained[images, "Entropy"];
Labeled[images[[Ordering[entropies, -10]]], "high entropy"]
Labeled[images[[Ordering[entropies, 10]]], "low entropy"]