Apprentissage non supervisé avec les auto-encodeurs : Nouveautés de Wolfram Language 11

Apprentissage non supervisé avec les auto-encodeurs

Entraînez un réseau auto-encodeur à reconstruire des images de chiffres manuscrits après les avoir projetées dans un espace vectoriel de « code » de dimension inférieure. Utilisez ces vecteurs de code pour effectuer le regroupement et la visualisation.

Tout d'abord obtenez les données d'apprentissage, puis sélectionnez les images correspondant aux chiffres 0 à 4.

In[1]:=

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]=

Obtenez l'« image moyenne » à soustraire des données d'apprentissage.

In[2]:=

trainingImages = Keys[trainingSubset];
meanImage = Image[Mean@Map[ImageData, trainingImages]]

Out[2]=

Créez un réseau à entraîner qui produit à la fois la reconstruction et l'erreur de reconstruction.

In[3]:=

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]=

Entraînez le réseau afin de minimiser l'erreur de reconstruction.

In[4]:=

trained = 
  If[$VersionNumber < 11.3, 
   NetTrain[net, <|"Input" -> trainingImages|>, "Loss"], 
   NetTrain[net, <|"Input" -> trainingImages|>, LossFunction -> "Loss"]
   ];

Out[4]=

Obtenez un sous-réseau qui exécute seulement les reconstructions.

In[5]:=

reconstructor = Take[trained, {NetPort["Input"], NetPort["Output"]}]

Out[5]=

Reconstruisez quelques exemples d'images.

In[6]:=

ImageAdd[reconstructor[#], meanImage] & /@ {\!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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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}}]\), \!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJI
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"], {{0, 28}, {28, 0}}, {
       0, 255},
ColorFunction->GrayLevel],
BoxForm`ImageTag[
      "Byte", ColorSpace -> Automatic, Interleaving -> None],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{28, 28},
PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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SWlpuYbL4gEEADLRqO8=
"], {{0, 28}, {28, 0}}, {0, 255},
ColorFunction->GrayLevel],
BoxForm`ImageTag[
      "Byte", ColorSpace -> Automatic, Interleaving -> None],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{28, 28},
PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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PLHAQxriP5vmF+ge/P//+a3SrY3Lb/3AlKEHAAAeUY84
"], {{0, 28}, {28, 0}}, {
       0, 255},
ColorFunction->GrayLevel],
BoxForm`ImageTag[
      "Byte", ColorSpace -> Automatic, Interleaving -> None],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{28, 28},
PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJI
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"], {{0, 28}, {28, 0}}, {0, 255},
ColorFunction->GrayLevel],
BoxForm`ImageTag[
      "Byte", ColorSpace -> Automatic, Interleaving -> None],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{28, 28},
PlotRange->{{0, 28}, {0, 28}}]\)}

Out[6]=

Obtenez un sous-réseau qui produit le vecteur de code.

In[7]:=

encoder = Take[trained, {NetPort["Input"], 4}]

Out[7]=

Calculez les codes pour toutes les images de test.

In[8]:=

testImages = Keys[testSubset];
features = encoder[testImages];

Projetez les vecteurs de code en trois dimensions et visualisez-les avec les classes originales (non vues par le réseau). Les classes de chiffres ont tendance à se regrouper.

In[9]:=

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]=

Effectuez la classification automatique directement sur les vecteurs de code et montrez un échantillon prélevé de chaque groupe.

In[10]:=

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

Out[10]=

Visualisez une classification hiérarchique des représentants aléatoires de chaque classe.

In[11]:=

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

In[12]:=

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"], {{0, 0}, {360, 258}}, {0,
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ColorFunction->RGBColor],
ImageSize->{360, 258},
PlotRange->{{0, 360}, {0, 258}}]\)

Wolfram Language™

Apprentissage non supervisé avec les auto-encodeurs

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