멀티태스킹 학습
CIFAR-100 데이터 집합의 이미지 라벨과 하위 라벨을 분류하는 합성곱 네트워크를 훈련합니다.
훈련 데이터 집합을 취득합니다.
In[1]:=
obj = ResourceObject["CIFAR-100"];
trainingData = ResourceData[obj, "TrainingDataset"];
RandomSample[trainingData, 5]
Out[1]=
이미지의 라벨 및 하위 라벨을 취득합니다.
In[2]:=
labels = Union@Normal@trainingData[All, "Label"]
sublabels = Union@Normal@trainingData[All, "SubLabel"]
Out[2]=
Out[2]=
간단한 합성곱 네트워크를 정의합니다.
In[3]:=
convnet = NetChain[{
ConvolutionLayer[20, {5, 5}],
ElementwiseLayer[Ramp],
PoolingLayer[{2, 2}, {2, 2}],
ConvolutionLayer[50, {5, 5}],
ElementwiseLayer[Ramp],
PoolingLayer[{2, 2}, {2, 2}],
FlattenLayer[],
DotPlusLayer[500],
ElementwiseLayer[Ramp]
}, "Input" -> NetEncoder[{"Image", {32, 32}}]]
Out[3]=
라벨 및 하위 라벨의 예측을 실시하여 합성곱 네트워크의 결과를 사용하는 네트워크를 생성합니다.
In[4]:=
net = NetGraph[{convnet, 100, SoftmaxLayer[], 20,
SoftmaxLayer[]}, {NetPort["Image"] ->
1 -> 2 -> 3 -> NetPort["SubLabel"],
2 -> 4 -> 5 -> NetPort["Label"]},
"Label" -> NetDecoder[{"Class", labels}],
"SubLabel" -> NetDecoder[{"Class", sublabels}]]
Out[4]=
네트워크를 훈련하고 두 출력에 교차 엔트로피의 손실 함수 추가를 NetTrain이 자동으로 취합니다.
In[5]:=
net = NetTrain[net, trainingData]
Out[6]=
이미지를 분류하고 라벨과 하위 라벨 모두를 얻습니다.
In[7]:=
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Out[7]=
"SubLabel" 출력에 가장 알맞는 분류를 취득합니다.
In[8]:=
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Out[8]=