用python写一段代码：采用ResNet网络模型对CIFA-100数据集分类，进行模型训练和模型评估

以下是用Python编写的使用ResNet网络模型对CIFAR-100数据集进行分类的示例代码。我们将使用PyTorch深度学习框架。

首先，我们将导入必要的库和模块：

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

接下来，我们将定义我们的ResNet网络模型。我们将使用预训练的ResNet18模型，并将其修改为适合CIFAR-100数据集的大小和类别数：

class ResNet18(nn.Module):
    def __init__(self):
        super(ResNet18, self).__init__()
        self.resnet = torchvision.models.resnet18(pretrained=True)
        self.resnet.fc = nn.Linear(512, 100)

    def forward(self, x):
        x = self.resnet(x)
        return x

然后，我们将进行数据预处理。我们将对图像进行随机裁剪、水平翻转、归一化和转换为张量：

transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

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

trainset = torchvision.datasets.CIFAR100(root='./data', train=True,
                                        download=True, transform=transform_train)

trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR100(root='./data', train=False,
                                       download=True, transform=transform_test)

testloader = torch.utils.data.DataLoader(testset, batch_size=128,
                                         shuffle=False, num_workers=2)

接下来，我们将定义我们的训练函数。我们将使用交叉熵损失函数和随机梯度下降优化器。我们还将在每个epoch中输出训练和测试的准确率：

def train(net, epochs):
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)

    for epoch in range(epochs):
        net.train()
        train_loss = 0
        correct = 0
        total = 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()

            train_loss += loss.item()
            _, predicted = outputs.max(1)
            total += labels.size(0)
            correct += predicted.eq(labels).sum().item()

        print('Epoch %d train loss: %.3f accuracy: %.3f' %
              (epoch + 1, train_loss / len(trainloader), 100.0 * correct / total))

        net.eval()
        test_loss = 0
        correct = 0
        total = 0

        with torch.no_grad():
            for data in testloader:
                inputs, labels = data
                outputs = net(inputs)
                loss = criterion(outputs, labels)

                test_loss += loss.item()
                _, predicted = outputs.max(1)
                total += labels.size(0)
                correct += predicted.eq(labels).sum().item()

        print('Epoch %d test loss: %.3f accuracy: %.3f' %
              (epoch + 1, test_loss / len(testloader), 100.0 * correct / total))

最后，我们将实例化我们的ResNet18模型，并开始训练和评估：

net = ResNet18()
train(net, 20)

这将对模型进行20个epoch的训练，并输出训练和测试准确率。