python玉桂狗代码怎么写

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玉桂狗是一个神经网络模型，用于图像识别任务。以下是一个简单的示例代码，用于训练和测试玉桂狗模型。

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 定义玉桂狗模型 
class JadeDog(nn.Module):
    def __init__(self):
        super(JadeDog, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 8 * 8, 128)
        self.fc2 = nn.Linear(128, 10)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.pool(x)
        x = x.view(-1, 64 * 8 * 8)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

# 数据预处理和加载 
transform = transforms.Compose([transforms.RandomHorizontalFlip(),  # 随机水平翻转 
    transforms.ToTensor(),  # 转为张量 
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  # 标准化 
])

train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)

# 初始化模型和优化器 
model = JadeDog()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
criterion = nn.CrossEntropyLoss()

# 训练模型 
def train(model, optimizer, criterion, train_loader):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

# 测试模型 
def test(model, criterion, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    accuracy = 100. * correct / len(test_loader.dataset)
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(test_loss, correct, len(test_loader.dataset), accuracy))

# 开始训练和测试 
for epoch in range(1, 11):
    train(model, optimizer, criterion, train_loader)
    test(model, criterion, test_loader)