pytorch工具——使用pytorch构建一个分类器

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

在GPU上训练模型