Aprendizado não supervisionado com Autoencoders
Treine uma rede autoencoder para reconstruir imagens de dígitos escritos à mão depois de projetá-los através de um "código" de espaço vetorial de menor dimensão. Use estes vetores de código para fazer agrupamentos e visualizações.
Primeiro obtenha os dados de treinamento e em seguida selecione as imagens correspondentes aos dígitos de 0 a 4.
resource = ResourceObject["MNIST"];
trainingData = ResourceData[resource, "TrainingData"];
trainingSubset = Select[trainingData, Last[#] <= 4 &];
testData = ResourceData[resource, "TestData"];
testSubset = Select[testData, Last[#] <= 4 &];
RandomSample[trainingSubset, 8]
Obtenha a "imagem média" para subtrair os dados de treinamento.
trainingImages = Keys[trainingSubset];
meanImage = Image[Mean@Map[ImageData, trainingImages]]
Crie uma rede para treinar que produza a reconstrução e o erro de reconstrução.
net = NetGraph[
{FlattenLayer[], 50, Ramp, 784, Tanh, ReshapeLayer[{1, 28, 28}],
MeanSquaredLossLayer[]},
{1 -> 2 -> 3 -> 4 -> 5 -> 6 -> NetPort["Output"],
6 -> NetPort[7, "Input"], NetPort["Input"] -> NetPort[7, "Target"]},
"Input" ->
NetEncoder[{"Image", {28, 28}, "Grayscale",
"MeanImage" -> meanImage}],
"Output" -> NetDecoder[{"Image", "Grayscale"}]
]
Treine a rede para minimizar o erro de reconstrução.
trained =
If[$VersionNumber < 11.3,
NetTrain[net, <|"Input" -> trainingImages|>, "Loss"],
NetTrain[net, <|"Input" -> trainingImages|>, LossFunction -> "Loss"]
];
Obtenha uma sub-rede que realize só reconstrução.
reconstructor = Take[trained, {NetPort["Input"], NetPort["Output"]}]
Reconstrua algumas imagens de amostras.
ImageAdd[reconstructor[#], meanImage] & /@ {\!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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"], {{0, 28}, {28, 0}}, {0, 255},
ColorFunction->GrayLevel],
BoxForm`ImageTag[
"Byte", ColorSpace -> Automatic, Interleaving -> None],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{28, 28},
PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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"], {{0, 28}, {28, 0}}, {
0, 255},
ColorFunction->GrayLevel],
BoxForm`ImageTag[
"Byte", ColorSpace -> Automatic, Interleaving -> None],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{28, 28},
PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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"], {{0, 28}, {28, 0}}, {0, 255},
ColorFunction->GrayLevel],
BoxForm`ImageTag[
"Byte", ColorSpace -> Automatic, Interleaving -> None],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{28, 28},
PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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"], {{0, 28}, {28, 0}}, {
0, 255},
ColorFunction->GrayLevel],
BoxForm`ImageTag[
"Byte", ColorSpace -> Automatic, Interleaving -> None],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{28, 28},
PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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"], {{0, 28}, {28, 0}}, {0, 255},
ColorFunction->GrayLevel],
BoxForm`ImageTag[
"Byte", ColorSpace -> Automatic, Interleaving -> None],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{28, 28},
PlotRange->{{0, 28}, {0, 28}}]\)}
Obtenha uma sub-rede que produza o vetor do código.
encoder = Take[trained, {NetPort["Input"], 4}]
Calcule os códigos para todas as imagens de teste.
testImages = Keys[testSubset];
features = encoder[testImages];
Projete os vetores de código em três dimensões e visualize-os junto com as classes originais (não vistas pela rede). As classes de dígitos tendem a se agrupar.
coords = DimensionReduce[features, 3];
classes = Values[testSubset];
Table[Extract[coords, Position[classes, i]], {i, 0, 4}]
ListPointPlot3D[
Table[Extract[coords, Position[classes, i]], {i, 0, 4}],
PlotLegends -> PointLegend[96, Range[0, 4]], BoxRatios -> 1,
Axes -> None, Boxed -> True,
PlotStyle -> Map[ColorData[96], Range[1, 5]], AspectRatio -> 1]
Realize agrupamento automático diretamente em vetores de código e mostre uma amostra tirada de cada grupo.
components = ClusteringComponents[features, 5, 1];
Map[Part[testImages, RandomSample[#, 10]] &,
PositionIndex[components]]
Visualize um agrupamento hierárquico de representantes aleatórios de cada classe.
representatives =
Catenate@GroupBy[testSubset, Last -> First, RandomSample[#, 6] &];
ClusteringTree[
encoder[representatives] -> Map[ImageCrop, representatives]]
\!\(\*
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