一、Loading and normalizing CIFAR10 加载并标准化CIFAR10

import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

注意：
If running on Windows and you get a BrokenPipeError, try setting the num_worker of torch.utils.data.DataLoader() to 0.
如果在Windows上运行时遇到BrokenPipeError，请尝试设置torch.utils.data.DataLoader（）为0。

下载训练集和测试集

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=False, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=False, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)

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

二、Define a Convolutional Neural Network 定义卷积神经网络

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        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 = 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

接下来的所有代码都将在if name == ‘main’:中进行抒写，原因可参考点击查看文章

三、Define a Loss function and optimizer 定义损失函数和优化器

四、Train the network训练网络

五、Test the network on the test data 在测试数据上测试网络

if __name__ == '__main__':

	# 展示一些训练图像
    # functions to show an image
    def imshow(img):
        img = img / 2 + 0.5     # unnormalize
        npimg = img.numpy()
        plt.imshow(np.transpose(npimg, (1, 2, 0)))
        plt.show()

    # get some random training images
    dataiter = iter(trainloader)
    images, labels = dataiter.next()

    # show images
    imshow(torchvision.utils.make_grid(images))
    # print labels
    print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))

    net = Net()

    # 3.Define a Loss function and optimizer使用分类交叉熵损失和带有动量的SGD
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

    """训练完后保存注释掉，方便后续代码的执行
    # 4.Train the network训练网络
    for epoch in range(5):  # loop over the dataset multiple times

        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels = data

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            if i % 2000 == 1999:  # print every 2000 mini-batches
                print('[%d, %5d] loss: %.3f' %
                      (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0

    print('Finished Training')

    PATH = './cifar_net.pth'
    torch.save(net.state_dict(), PATH)
    """

    # 5. Test the network on the test data在测试数据上测试网络
    # 首先第一步显示测试集中的图像以使其熟悉 32-47
    # 第二步重新加载保存的模型 71-104
    outputs = net(images)
    _, predicted = torch.max(outputs, 1)

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

    # 查看网络在整个数据集上的表现
    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            outputs = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

    # 哪些类的表现良好，哪些类的表现不佳
    class_correct = list(0. for i in range(10))
    class_total = list(0. for i in range(10))
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            outputs = net(images)
            _, predicted = torch.max(outputs, 1)
            c = (predicted == labels).squeeze()
            for i in range(4):
                label = labels[i]
                class_correct[label] += c[i].item()
                class_total[label] += 1

    for i in range(10):
        print('Accuracy of %5s : %2d %%' % (classes[i], 100 * class_correct[i] / class_total[i]))