CIFARNet在Python环境下的图像分类算法

CIFARNet是一种由加拿大计算机科学与工程研究所（CIFAR）开发的卷积神经网络模型，用于图像分类任务。它是为了解决CIFAR-10和CIFAR-100数据集中的图像分类问题而设计的。CIFAR-10数据集包含10个不同类别的60000张32x32彩色图片，每个类别包含6000张图片。CIFAR-100数据集包含100个不同类别的60000张图片，每个类别包含600张图片。

下面是CIFARNet的使用例子：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# 定义CIFARNet网络模型
class CIFARNet(nn.Module):
    def __init__(self):
        super(CIFARNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.relu1 = nn.ReLU()
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.relu2 = nn.ReLU()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 16 * 16, 512)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        x = self.relu1(self.conv1(x))
        x = self.pool(self.relu2(self.conv2(x)))
        x = x.view(-1, 64 * 16 * 16)
        x = self.relu3(self.fc1(x))
        x = self.fc2(x)
        return x

# 准备CIFAR-10数据集
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=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=32,
                                         shuffle=False, num_workers=2)

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

# 初始化网络模型和优化器
net = CIFARNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练网络
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 2000 == 1999:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished training')

# 在测试集上评估模型准确率
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %.2f %%' % (100 * correct / total))

# 计算各个类别的准确率
class_correct = list(0. for _ in range(10))
class_total = list(0. for _ 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 : %.2f %%' % (classes[i], 100 * class_correct[i] / class_total[i]))

上述代码首先定义了CIFARNet网络模型，该模型包含两个卷积层，一个池化层和两个全连接层。然后，使用torchvision库加载CIFAR-10数据集，并对数据进行预处理。接下来，初始化网络模型、损失函数和优化器。然后，使用训练集对模型进行训练，并在每个epoch结束时打印损失值。最后，在测试集上评估模型的准确率，并计算各个类别的准确率。

CIFARNet是一个简单而有效的图像分类算法。你可以根据需要进行修改和扩展，例如增加更多的卷积层或全连接层，或者使用不同的优化器和损失函数。希望这个例子能够帮助你开始使用CIFARNet进行图像分类任务。