Evite o sobreajuste usando um conjunto de validação de retenção
Use a opção ValidationSet para NetTrain para se assegurar de que a rede treinada não sobreajuste os dados de entrada. Isto normalmente é conhecido como um teste ou conjuntos de dados de retenção.
Crie dados de treinamento sintéticos baseados em uma curva de Gauss.
data = Table[
x -> Exp[-x^2] + RandomVariate[NormalDistribution[0, .15]], {x, -3,
3, .2}];
plot = ListPlot[List @@@ data, PlotStyle -> Red]
Treine uma rede com um grande número de parâmetros relativos à quantidade de dados de treinamento.
net = NetChain[{150, Tanh, 150, Tanh, 1}, "Input" -> "Scalar",
"Output" -> "Scalar"];
net1 = NetTrain[net, data, Method -> "ADAM"]
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ColorFunction->RGBColor],
BoxForm`ImageTag[
"Byte", ColorSpace -> "RGB", Interleaving -> True,
Magnification -> 0.5],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSize->Magnification[0.5],
ImageSizeRaw->{560, 706},
PlotRange->{{0, 560}, {0, 706}}]\)
A rede resultante sobreajusta os dados, aprendendo o ruído e também a função subjacente.
Show[Plot[net1[x], {x, -3, 3}], plot]
Subdivida os dados em um conjunto de treinamento e um conjunto de validação de retenção.
data = RandomSample[data];
{train, test} = TakeDrop[data, 24];
Use a opção ValidationSet para que NetTrain selecione a rede que obteve a menor perda de validação durante o treinamento.
net2 = NetTrain[net, train, ValidationSet -> test]
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"], {{0, 750}, {562, 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag[
"Byte", ColorSpace -> "RGB", Interleaving -> True,
Magnification -> 0.5],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSize->Magnification[0.5],
ImageSizeRaw->{562, 750},
<<<<<<< avoid-overfitting-using-a-hold-out-set.html.pt-br
PlotRange->{{0, 562}, {0, 750}}]\)
O resultado dado por NetTrain foi a rede que generalizou melhor os pontos no conjunto de validação, conforme medido pela perda de validação. Isso penaliza o sobreajuste, já que ruído presente nos dados de treinamento não está correlacionado com o ruído presente no conjunto de validação.
Show[Plot[net2[x], {x, -3, 3}], plot]
Outra maneira de resolver o sobreajuste é usar a regularização L2, que associa implicitamente uma perda com parâmetros diferentes de zero na rede durante o treinamento. Isto pode ser especificado com a opção Method para NetTrain.
net3 = NetTrain[net, data,
Method -> {"ADAM", "L2Regularization" -> 0.01}]
A regularização L2 penaliza redes "complexas", conforme são medidas pelas magnitudes dos seus parâmetros, o que tende a reduzir sobreajuste.
Show[Plot[net3[x], {x, -3, 3}], plot]