resnet cifar10

### 关于ResNet模型在CIFAR-10数据集上的实现

对于ResNet模型应用于CIFAR-10数据集的情况，通常会采用简化版本的ResNet架构来适应较小规模的数据集和图像尺寸。以下是基于PyTorch框架的一个简单示例代码片段用于训练ResNet模型：

import torch
import torchvision.transforms as transforms
from torchvision.datasets import CIFAR10
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from torchvision.models.resnet import resnet18


def prepare_data():
    transform_train = transforms.Compose([
        transforms.RandomCrop(32, padding=4),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

    transform_test = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])
    
    trainset = CIFAR10(root='./data', train=True, download=True, transform=transform_train)
    testset = CIFAR10(root='./data', train=False, download=True, transform=transform_test)

    trainloader = DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
    testloader = DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)

    return trainloader, testloader


def create_model(num_classes=10):
    model = resnet18(pretrained=False)
    model.fc = nn.Linear(model.fc.in_features, num_classes)
    return model.cuda()


train_loader, test_loader = prepare_data()
model = create_model()

criterion = nn.CrossEntropyLoss().cuda()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)


for epoch in range(20):  
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data[0].cuda(), data[1].cuda()

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        
print('Finished Training')

此代码展示了如何加载并预处理CIFAR-10数据集，创建一个未预先训练过的ResNet18网络结构，并对其进行编译与训练过程[^2]。