分类器任务和数据介绍
训练分类器的步骤
#1
import torch
import torchvision
import torchvision.transforms as transforms
transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))]) #三个部分的数据的均值,标准差都为0.5
trainset=torchvision.datasets.CIFAR10(root='./data1',train=True,download=True,transform=transform)
trainloader=torch.utils.data.DataLoader(trainset,batch_size=4,shuffle=True)
testset=torchvision.datasets.CIFAR10(root='./data1',train=False,download=True,transform=transform)
testloader=torch.utils.data.DataLoader(testset,batch_size=4,shuffle=True)
classes=('plane','car','bird','cat','deer','dog','frog','horse','ship','truck')
展示若干训练集图片
#2
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net,self).__init__()
self.conv1=nn.Conv2d(3,6,5)
self.conv2=nn.Conv2d(6,16,5)
self.pool=nn.MaxPool2d(2,2)
self.fc1=nn.Linear(16*5*5,120)
self.fc2=nn.Linear(120,84)
self.fc3=nn.Linear(84,10)
def forward(self,x):
x=self.pool(F.relu(self.conv1(x)))
x=self.pool(F.relu(self.conv2(x)))
x=x.view(-1,16*5*5)
x=F.relu(self.fc1(x))
x=F.relu(self.fc2(x))
x=self.fc3(x)
return x
net=Net()
print(net)
#3
import torch.optim as optim
criterion=nn.CrossEntropyLoss()
optimizer=optim.SGD(net.parameters(),lr=0.001,momentum=0.9)
#4
for epoch in range(2):
running_loss=0.0
#按批次迭代训练模型
for i,data in enumerate(trainloader,0):
inputs,labels=data
optimizer.zero_grad()
outputs=net(inputs)
loss=criterion(outputs,labels)
loss.backward()
optimizer.step()
#打印训练信息
running_loss+=loss.item()
if (i+1)%2000==0:
print('[%d,%5d] loss:%.3f'%(epoch+1,i+1,running_loss/2000))
running_loss=0
print('finished training')
#设定模型保存位置
PATH='./cifar_net.pth'
#保存模型的状态字典
torch.save(net.state_dict(),PATH)
#5
dataiter=iter(testloader)
images,labels=next(dataiter)
print('groundtrue:',' '.join('%5s'%classes[labels[j]] for j in range(4)))
#加载模型参数,在测试阶段
net.load_state_dict(torch.load(PATH))
#利用模型对图片进行预测
outputs=net(images)
_,predicted=torch.max(outputs,1)
print('predicted:',''.join('%5s'%classes[predicted[j]] for j in range(4)))
#5
#在整个测试集上测试模型准确率
correct=0
total=0
with torch.no_grad():
for data in testloader:
images,labels=data
outputs=net(images)
_,predicted=torch.max(outputs.data,1) #_是最大值,predicted是最大值下标
total+=labels.size(0)
correct+=(predicted==labels).sum().item()
print('accuracy of the network on the 10000 test images:%d %%'%(100*correct/total))
注
分别测试不同类别的模型准确率
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在GPU上训练模型
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