Wolfram 语言

神经网络

将图像视为一个函数来建模

训练一个网络,通过将图像视为从像素位置 (x, y) 到颜色值 (r, g, b) 的函数来再现图像.

将图像转换为联系归一化的像素位置与 RGB 值的规则列表.

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]=

创建一个根据像素位置来预测颜色的网络.

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

训练这个网络.

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

用该网络预测整个原始图像.

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]=

图示组合起来以产生最终图像的特征映射.

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]=

相关范例

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