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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) 以上代码使用PyTorch库构建了一个简单的玉桂狗模型，并使用CIFAR-10数据集进行训练和测试。你可以根据实际需要进行修改和扩展。
