Wolfram Language

Redes neuronales

Aprendizaje no supervisado con Autoencoders

Entrene una red autoencoder para reconstruir dígitos de escritura a mano después de que se proyectan a través de un "código" de espacio vectorial dimensional inferior. Utilice estos vectores para realizar agrupamientos y visualizaciones.

Primero obtenga datos de entrenamiento, y luego seleccione las imágenes correspondientes a los dígitos del 0 al 4.

In[1]:=
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resource = ResourceObject["MNIST"]; trainingData = ResourceData[resource, "TrainingData"]; trainingSubset = Select[trainingData, Last[#] <= 4 &]; testData = ResourceData[resource, "TestData"]; testSubset = Select[testData, Last[#] <= 4 &]; RandomSample[trainingSubset, 8]
Out[1]=

Obtenga la "imagen promedio" para sustraer de los datos de entrenamiento.

In[2]:=
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trainingImages = Keys[trainingSubset]; meanImage = Image[Mean@Map[ImageData, trainingImages]]
Out[2]=

Cree una red para entrenar que produzca la reconstrucción y el error de reconstrucción.

In[3]:=
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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"}] ]
Out[3]=

Entrene la red para minimizar el error de reconstrucción.

In[4]:=
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trained = If[$VersionNumber < 11.3, NetTrain[net, <|"Input" -> trainingImages|>, "Loss"], NetTrain[net, <|"Input" -> trainingImages|>, LossFunction -> "Loss"] ];
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Obtenga una subred que realice sólo reconstrucciones.

In[5]:=
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reconstructor = Take[trained, {NetPort["Input"], NetPort["Output"]}]
Out[5]=

Reconstruya algunas imágenes de muestra.

In[6]:=
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ImageAdd[reconstructor[#], meanImage] & /@ {\!\(\* GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJI mQwU/x9A8CWTyfQBDrlbLCxMU7BLvbLCKTfRkQUoFzbpIBY5JhaQHAuL8hkM KU8GJiAQVQQS6FIHlED6cjYdrGdhmYYqdV8CaJxy2df//x9IsvD0/UJ3vvNr MHMSUNUdNDmzhxDmA3N0OYQL7psyMEUjyxWzsMCYGGaqweReHQC6SvIhVrl8 oE+UDv3HJuepBJTzRvWfKhPTtm3STEzgwEGV+t8HDkoIkYMmBwwNiJyk052v aHL/DxZC5LDH3/ZAlqAd2x9ilaMPAABwyokL "], {{0, 28}, {28, 0}}, {0, 255}, ColorFunction->GrayLevel], BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{28, 28}, PlotRange->{{0, 28}, {0, 28}}]\), \!\(\* GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJI mQwU/x9gcJNxEk65FcxrccpV8OGUusSdiVNuNeMBnHKmCl9wSd1nVMepbQGj DU65YsZNuKSOCRl9xyXXzBCF08gQxnW4pJ6La+DU1s6YgFMug7EQp5w0415c UodYcMsVMRr9wSH1VYOxDZe2X5b+X3E6hS4AAAjL8xI= "], {{0, 28}, {28, 0}}, { 0, 255}, ColorFunction->GrayLevel], BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{28, 28}, PlotRange->{{0, 28}, {0, 28}}]\), \!\(\* GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJI mQwU/6cp6G4KYWDIXIRNKowJDFQfYpfSKvRnYmrFkDrNyqR7//P/n4ZMxRhy m1h0nwGpVnamfZhmPngLIvWYsMmBQRcHk+VX7FKbOZgkDuDQVs/ElI9Dyp+T KeEzdqlnokxid3Bos2RiKsIhtZGdyQmHiW/McWurZGIKwqHtPzsT0zMcUiC5 K6+B4Nf/X69f38rMzMz5iiwHBuEFkVBWC1wukAkB2Dg4wrq6jiEM7WxpaQFp SWlpuYbL4gEEADLRqO8= "], {{0, 28}, {28, 0}}, {0, 255}, ColorFunction->GrayLevel], BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{28, 28}, PlotRange->{{0, 28}, {0, 28}}]\), \!\(\* GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJI mQwU/x8Q8Plabu55HFJ1TExMrDFvsclVMYGB1E4scrOYmPNmThFi4qj/iiHn xRQBJA+JMDHF/EKXY2C6AqKO2jExRf9Gk3Nhug+mTwgzMa1Ek8sFys12XLFi RQ4TUwua3EKmjdfZIW5lUlj9AUXu4+LPd/iZYIB7Gbprt8T5wGX1rqC79s/7 9zduvn+fx8PE5IYagK+PwlhH9ZiYPJClNilxbICxP6kw8W9HklvKwcR8DMY5 xcLkgKyxm4npCIw9h5kpD8UfzkxKiTdBrAk6nEyocv8/K3EysbABAcgX5m/Q PLHAQxriP5vmF+ge/P//+a3SrY3Lb/3AlKEHAAAeUY84 "], {{0, 28}, {28, 0}}, { 0, 255}, ColorFunction->GrayLevel], BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{28, 28}, PlotRange->{{0, 28}, {0, 28}}]\), \!\(\* GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJI mQwU/x+U4Kw8lLHzEYZcmwSUkR2OLvXbBiY3X/cLmtwu5kooq5f5FarUJWG1 z1CmPbpcOMcpKOstAxOq3GpeHRiziMnpF4pcGPNUKOu+OOteFKkPcswwZiWz DorU/5fM0QgTIlDlvhnrvYWqYmKagioHVG2xCgjqom2Y4TbDwLVQLmYgEJdg YWb+9h8dnFsNBP//xzEzY0jBQAMz8yVccvVMTPj04ZSrYObGKScuPAGnnM9e nFJUAQDVl858 "], {{0, 28}, {28, 0}}, {0, 255}, ColorFunction->GrayLevel], BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{28, 28}, PlotRange->{{0, 28}, {0, 28}}]\)}
Out[6]=

Obtenga una subred que produzca el vector de código.

In[7]:=
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encoder = Take[trained, {NetPort["Input"], 4}]
Out[7]=

Calcule los códigos para todas las imágenes de prueba.

In[8]:=
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testImages = Keys[testSubset]; features = encoder[testImages];

Proyecte los vectores de código en tres dimensiones y visualícelos junto con las clases originales (no vistas por la red). Las clases de dígitos tienen a agruparse.

In[9]:=
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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]
Out[9]=

Realice agrupamiento automático directamente en vectores de código y muestre una muestra tomada de cada grupo.

In[10]:=
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components = ClusteringComponents[features, 5, 1]; Map[Part[testImages, RandomSample[#, 10]] &, PositionIndex[components]]
Out[10]=

Visualice un agrupamiento jerárquico de representantes aleatorios de cada clase.

In[11]:=
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representatives = Catenate@GroupBy[testSubset, Last -> First, RandomSample[#, 6] &]; ClusteringTree[ encoder[representatives] -> Map[ImageCrop, representatives]]
In[12]:=
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