Classificação de imagens fora do núcleo
Treine uma rede para distinguir os algarismos 1 de 2, e carregar apenas pequenos lotes de imagens do disco para a memória um de cada vez.
Baixe um conjunto de imagens e descompacte-os.
In[1]:=
zip = URLDownload["https://wolfr.am/ebyHmnkR", "characters12.zip"];
dir = CreateDirectory[];
ExtractArchive[First @ zip, dir] // LengthOut[1]=
Obtenha a rota dos arquivo de imagem e obtenha as classes dos nomes das pastas.
In[2]:=
loadFiles[dir_] :=
Map[File[#] -> FileNameTake[#, {-2}] &,
FileNames["*.jpg", dir, Infinity]];
trainingData = loadFiles[FileNameJoin[{dir, "characters", "train"}]];
testData = loadFiles[FileNameJoin[{dir, "characters", "test"}]];O dados de treinamento correspondem a uma lista de regras de objetos de File a classes.
In[3]:=
RandomSample[trainingData, 3]Out[3]=
Defina uma rede simples.
In[4]:=
net = NetChain[
{FlattenLayer[], DotPlusLayer[2], SoftmaxLayer[]},
"Output" -> NetDecoder[{"Class", {"1", "2"}}],
"Input" -> NetEncoder[{"Image", {28, 28}, "Grayscale"}]
]Out[4]=
Treine a rede.
In[5]:=
trained =
NetTrain[net, trainingData, ValidationSet -> testData,
MaxTrainingRounds -> 600, BatchSize -> 64]Out[6]=
Classifique imagens diretamente da rota de arquivos.
In[7]:=
ims = Keys@RandomSample[testData, 3]
trained /@ imsOut[7]=
Out[7]=
Ou avalie diretamente em uma imagem e obtenha as probabilidades de classificação.
In[8]:=
trained[\!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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"], {{0, 28}, {
28, 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSize->Automatic,
ImageSizeRaw->{28, 28},
PlotRange->{{0, 28}, {0, 28}}]\), "Probabilities"]Out[8]=
