Wolfram Language

Redes neuronales

Modele una imagen como una función

Entrene una red para reproducir una imagen mediante el tratamiento de la imagen como una función de posiciones de píxeles (x, y) a valores de color (r, g, b).

Convierta la imagen estándar "Lena" en una lista de reglas asociando posiciones de píxeles normalizadas con valores RGB.

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]=

Cree una red para predecir el color basado en posiciones de píxeles.

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

Entrene la red.

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

Utilice la red para predecir la imagen original completa.

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]=

Visualice los mapas de características que son combinados para producir la imagen final.

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]=

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