Classificação de objetos
Use o banco de dados CIFAR-10 de imagens classificadas, treine uma rede convolucional para prever a classe de cada objeto.
Primeiro obtenha os dados de treinamento.
In[1]:=
obj = ResourceObject["CIFAR-10"];
trainingData = ResourceData[obj, "TrainingData"];
RandomSample[trainingData, 5]
Out[1]=
Extraia as classes únicas.
In[2]:=
classes = Union@Values[trainingData]
Out[2]=
Crie uma rede convolucional que prevê a classe de dada uma imagem.
In[3]:=
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]=
Treine a rede nos dados de treinamento.
In[4]:=
trained = NetTrain[lenet, trainingData, MaxTrainingRounds -> 4]
Out[5]=
Preveja as classes mais prováveis para um conjunto de imagens.
In[6]:=
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Out[6]=
Para um exemplo específico, dê as probabilidades das atribuições do marcador mais prováveis.
In[7]:=
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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}]
Out[7]=
Pegue uma amostra aleatória, selecione as imagens para as quais a rede produz maiores e menores previsões de entropia. Inputs de alta entropia pode ser interpretados como aqueles para os quais a rede está mais incerta sobre a classe correta.
In[8]:=
images = RandomSample[Keys[trainingData], 5000];
In[9]:=
entropies = trained[images, "Entropy"];
In[10]:=
Labeled[images[[Ordering[entropies, -10]]], "high entropy"]
Labeled[images[[Ordering[entropies, 10]]], "low entropy"]
Out[10]=
Out[10]=