告密者的下场

nn.ReLU()

)

self.max_pooling2_1=nn.MaxPool2d(kernel_size=2,stride=1)

#4*4

self.conv2_2=nn.Sequential(

nn.Conv2d(24,24,kernel_size=3,stride=1,padding=1),

nn.ReLU()

)

self.max_pooling2=nn.MaxPool2d(kernel_size=2,stride=2)

#2*2

#2*2

self.fc=nn.Linear(24*2*2,2)

defforward(self,x):

batchsize=x.size(0)

out=self.conv1(x)

out=self.max_pooling1(out)

out=self.conv2_1(out)

out=self.conv2_2(out)

out=self.max_pooling2(out)

out=out.view(batchsize,-1)

out=self.fc(out)

out=F.log_softmax(out,dim=1)

returnout

classTrainingDataSet(Dataset):

def__init__(self):

super(TrainingDataSet,self).__init__()

self.data_dict_X=X_train

self.data_dict_y=y_train

def__getitem__(self,index):

t=self.data_dict_

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