告密者的下场
告密者的下场 (第1/2页)从前有座山，叫巴里赫德，他一个当一座山，十分的爽。
哎，想不到吧。
这个山里面有树林，有庙，有这个山里灵活的狗。山上有棵树，这棵树不叫高树，因为这个梗太老了。这棵树的形状有些奇特，大概就长这个样子。
importtorch
fromtorchimportnn
importtorch.nn.functionalasF
importos
importtensorboardX
fromtorch.utils.dataimportDataset
fromtorch.utils.dataimportDataLoader
device=torch.device('cuda'iftorch.cuda.is_available()else'cpu')
classVGGBaseSimpleS2(nn.Module):
def__init__(self):
super(VGGBaseSimpleS2,self).__init__()
self.conv1=nn.Sequential(
nn.Conv2d(1,12,kernel_size=3,stride=1,padding=1),
#nn.BatchNorm2d(16),
nn.ReLU()
)
#6*6
self.max_pooling1=nn.MaxPool2d(kernel_size=2,stride=1)
#5*5
self.conv2_1=nn.Sequential(
nn.Conv2d(12,24,kernel_size=3,stride=1,padding=1),
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_X[index,0:36]
t=torch.tensor(t).view(6,6)
returnt,self.data_dict_y[index]
def__len__(self):
returnlen(self.data_dict_y)
classTestDataSet(Dataset):
def__init__(self):
super(TestDataSet,self).__init__()
self.data_dict_X=X_validate
self.data_dict_y=y_validate
def__getitem__(self,index):
t=self.data_dict_X[index,0:36]
t=torch.tensor(t).view(6,6)
returnt,self.data_dict_y[index]
def__len__(self):
returnlen(self.data_dict_y)
defcnn_classification():
batch_size=256
trainDataLoader=DataLoader(TrainingDataSet(),batch_size=batch_size,shuffle=False)
testDataLoader=DataLoader(TestDataSet(),batch_size=batch_size,shuffle=False)
epoch_num=200
#lr=0.001
lr=0.001
net=VGGBaseSimpleS2().to(device)
print(net)
#loss
loss_func=nn.CrossEntropyLoss()
#optimizer
optimizer=torch.optim.Adam(net.parameters(),lr=lr)
#optimizer=torch.optim.SGD(net.parameters(),lr=lr,momentum=0.9,weight_decay=5e-4)
scheduler=torch.optim.lr_scheduler.StepLR(optimizer,step_size=5,gamma=0.9)
ifnotos.path.exists(“logCNN“):
os.mkdir(“logCNN“)
writer=tensorboardX.SummaryWriter(“logCNN“)
forepochinrange(epoch_num):
train_sum_loss=0
train_sum_correct=0
train_sum_fp=0
train_sum_fn=0
train_sum_tp=0
train_sum_tn=0
fori,datainenumerate(trainDataLoader):
net.train()
inputs,labels=data
inputs=inputs.unsqueeze(1).to(torch.float32)
labels=labels.type(torch.LongTensor)
inputs,labels=inputs.to(device),labels.to(device)
outputs=net(inputs)
loss=loss_func(outputs,labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
_,pred=torch.max(outputs.data,dim=1)
acc=pred.eq(labels.data).cpu().sum()
one=torch.ones_like(labels)
zero=torch.zeros_like(labels)
tn=((labels==zero)*(pred==zero)).sum()
tp=((labels==one)*(pred==one)).sum()
fp=((labels==zero)*(pred==one)).sum()
fn=((labels==one)*(pred==zero)).sum()
train_sum_fn+=fn.item()
train_sum_fp+=fp.item()
train_sum_tn+=tn.item()
train_sum_tp+=tp.item()
train_sum_loss+=loss.item()
train_sum_correct+=acc.item()
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