Wolfram Language

Redes neuronales

Evite el sobreajuste usando un conjunto de validación de retención

Utilice la opción ValidationSet de NetTrain para asegurarse de que la red entrenada no sobreajuste los datos de entrada. Este proceso se conoce comúnmente como una prueba o conjunto de datos de retención.

Cree datos de entrenamiento sintético basados en una curva gaussiana.

In[1]:=
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data = Table[ x -> Exp[-x^2] + RandomVariate[NormalDistribution[0, .15]], {x, -3, 3, .2}];
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plot = ListPlot[List @@@ data, PlotStyle -> Red]
Out[2]=

Entrene una red con un gran número de parámetros relativos a la cantidad de datos de entrenamiento.

In[3]:=
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net = NetChain[{150, Tanh, 150, Tanh, 1}, "Input" -> "Scalar", "Output" -> "Scalar"]; net1 = NetTrain[net, data, Method -> "ADAM"]
Out[3]=
In[4]:=
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"RGB", Interleaving -> True, Magnification -> 0.5], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSize->Magnification[0.5], ImageSizeRaw->{560, 706}, PlotRange->{{0, 560}, {0, 706}}]\)

La red resultante sobreajusta los datos, aprendiendo el ruido además de la función subyacente.

In[5]:=
Click for copyable input
Show[Plot[net1[x], {x, -3, 3}], plot]
Out[5]=

Subdivida los datos en un conjunto de entrenamiento y un conjunto de validación de retención.

In[6]:=
Click for copyable input
data = RandomSample[data]; {train, test} = TakeDrop[data, 24];

Utilice la opción ValidationSet para que NetTrain seleccione la red que alcance la pérdida de validación menor durante el entrenamiento.

In[7]:=
Click for copyable input
net2 = NetTrain[net, train, ValidationSet -> test]
Out[7]=
In[8]:=
Click for copyable input
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El resultado dado por NetTrain fue la red que mejor generalizó los puntos en el conjunto de validación, según lo medido por la pérdida de validación. Esto penaliza el sobreajuste, ya que el ruido presente en los datos de entrenamiento no está relacionado con el ruido presente en el conjunto de validación.

In[9]:=
Click for copyable input
Show[Plot[net2[x], {x, -3, 3}], plot]
Out[9]=

Otra forma de abordar el sobreajuste es usando la regularización L2, la cual asocia implícitamente una pérdida con parámetros distintos de cero en la red durante el entrenamiento. Esto puede ser especificado con una opción Method para NetTrain.

In[10]:=
Click for copyable input
net3 = NetTrain[net, data, Method -> {"ADAM", "L2Regularization" -> 0.01}]
Out[10]=

La regularización L2 penaliza redes "complejas", según son medidas por las magnitudes de sus parámetros, los cuales tienden a reducir el sobreajuste.

In[11]:=
Click for copyable input
Show[Plot[net3[x], {x, -3, 3}], plot]
Out[11]=

Ejemplos relacionados

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