Wolfram Language

Réseaux neuronaux

Modélisez une image comme une fonction

Entraînez un réseau pour reproduire une image par le traitement de l'image comme une fonction des positions de pixels de (x, y) et des valeurs de couleur (r, g, b).

Convertissez une image dans des listes de règles associant des positions de pixels normalisées avec les valeurs RVB.

In[1]:=
Click for copyable input
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In[2]:=
Click for copyable input
dims = Reverse@ImageDimensions[img]; rules = Flatten@ MapIndexed[(2 (#2 - 1.)/(dims - 1) - 1.) -> #1 &, ImageData@img, {2}];
In[3]:=
Click for copyable input
Take[rules, 5]
Out[3]=
In[4]:=
Click for copyable input
Length[rules]
Out[4]=

Créez un réseau pour prédire la couleur en fonction de la position du pixel.

In[5]:=
Click for copyable input
chain = NetChain[{100, Ramp, 100, Ramp, 50, Tanh, 3}, "Input" -> 2]
Out[5]=

Entraînez le réseau.

In[6]:=
Click for copyable input
trained = NetTrain[chain, rules, MaxTrainingRounds -> 200, BatchSize -> 2048];
Out[7]=

Utilisez le réseau pour prédire l'ensemble de l'image originale.

In[8]:=
Click for copyable input
{h, v} = Range[-1, 1, 2./#] & /@ dims; coords = Tuples[{h, v}]; Image[Partition[trained@coords, Length[v]]]
Out[8]=

Visualisez les cartes de caractéristiques qui sont combinées pour produire l'image finale.

In[9]:=
Click for copyable input
featureNet = Take[trained, 4] {h, v} = Range[-1, 1, 60./#] & /@ dims; coords = Tuples[{h, v}]; features = Image[Partition[featureNet@coords, Length[v]], Magnification -> 2]; Multicolumn[ColorSeparate[ImageAdjust@features], Spacings -> {0.2, 0.2}]
Out[9]=

Exemples connexes

de en es ja ko pt-br ru zh