PyTorch——CIFAR-10分类

import torchvision as tv
import torchvision.transforms as transforms
from torchvision.transforms import ToPILImage
show = ToPILImage() 

transform = transforms.Compose([
        transforms.ToTensor(), # 转为Tensor
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), # 归一化
                             ])

# 训练集
trainset = tv.datasets.CIFAR10(
                    root='/home/cy/tmp/data/', 
                    train=True, 
                    download=True,
                    transform=transform)

trainloader = t.utils.data.DataLoader(
                    trainset, 
                    batch_size=4,
                    shuffle=True, 
                    num_workers=2)

# 测试集
testset = tv.datasets.CIFAR10(
                    '/home/cy/tmp/data/',
                    train=False, 
                    download=True, 
                    transform=transform)

testloader = t.utils.data.DataLoader(
                    testset,
                    batch_size=4, 
                    shuffle=False,
                    num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

(data, label) = trainset[100]
print(classes[label])

# (data + 1) / 2是为了还原被归一化的数据
show((data + 1) / 2).resize((100, 100))

dataiter = iter(trainloader)
images, labels = dataiter.next() # 返回4张图片及标签
print(' '.join('%11s'%classes[labels[j]] for j in range(4)))
show(tv.utils.make_grid((images+1)/2)).resize((400,100))

定义网络
CIFAR-10是3通道彩图，修改self.conv1第一个参数为3

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.fc1   = nn.Linear(16*5*5, 120)  
        self.fc2   = nn.Linear(120, 84)
        self.fc3   = nn.Linear(84, 10)

    def forward(self, x): 
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) 
        x = F.max_pool2d(F.relu(self.conv2(x)), 2) 
        x = x.view(x.size()[0], -1) 
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)        
        return x


net = Net()
print(net)

定义损失函数及优化器

from torch import optim
criterion = nn.CrossEntropyLoss() # 交叉熵损失函数
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

训练网络

不断地执行如下流程：
输入数据
前向传播+反向传播
更新参数

t.set_num_threads(8)
for epoch in range(2):  
    
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        
        # 输入数据
        inputs, labels = data
        
        # 梯度清零
        optimizer.zero_grad()
        
        # forward + backward 
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()   
        
        # 更新参数 
        optimizer.step()
        
        # 打印log信息
        # loss 是一个scalar,需要使用loss.item()来获取数值，不能使用loss[0]
        running_loss += loss.item()
        if i % 2000 == 1999: # 每2000个batch打印一下训练状态
            print('[%d, %5d] loss: %.3f' \
                  % (epoch+1, i+1, running_loss / 2000))
            running_loss = 0.0
print('Finished Training')

dataiter = iter(testloader)
images, labels = dataiter.next() # 一个batch返回4张图片
print('实际的label: ', ' '.join(\
            '%08s'%classes[labels[j]] for j in range(4)))
show(tv.utils.make_grid(images / 2 - 0.5)).resize((400,100))

# 计算图片在每个类别上的分数
outputs = net(images)
# 得分最高的那个类
_, predicted = t.max(outputs.data, 1)

print('预测结果: ', ' '.join('%5s'\
            % classes[predicted[j]] for j in range(4)))

correct = 0 # 预测正确的图片数
total = 0 # 总共的图片数


# 由于测试的时候不需要求导，可以暂时关闭autograd，提高速度，节约内存
with t.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = t.max(outputs, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum()

print('10000张测试集中的准确率为: %d %%' % (100 * correct / total))

device = t.device("cuda:0" if t.cuda.is_available() else "cpu")

net.to(device)
images = images.to(device)
labels = labels.to(device)
output = net(images)
loss= criterion(output,labels)

loss