1.加载CIFAR-10数据集； 2.定义模型，这里我们使用Pytorch内置的GoogLeNet网络结构； 3.定义损失函数，这里我们使用交叉熵loss函数； 4.定义优化器，这里我们使用SGD优化器，并设置学习率和动量； 5.开始训练模型：在每个epoch中循环遍历训练集，计算前向传播、损失函数和反向传播，然后更新参数； 6.测试模型：在测试集中运行模型并计算准确度。 写出每个步骤的相关代码

以下是每个步骤的相关代码：

1. 加载CIFAR-10数据集

import torchvision.datasets as dset
import torchvision.transforms as transforms

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 = dset.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)

testset = dset.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)

2. 定义模型

import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.googlenet import googlenet

class GoogLeNet(nn.Module):
    def __init__(self, num_classes=10):
        super(GoogLeNet, self).__init__()
        self.inception = googlenet(pretrained=False)
        self.inception.fc = nn.Linear(1024, num_classes)

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

net = GoogLeNet()

3. 定义损失函数

criterion = nn.CrossEntropyLoss()

4. 定义优化器

import torch.optim as optim

learning_rate = 0.1
momentum = 0.9
optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=momentum)

5. 开始训练模型

for epoch in range(100):

    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()

    print('[Epoch %d] loss: %.3f' % (epoch + 1, running_loss / len(trainloader)))

print('Finished Training')

6. 测试模型

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))