如何在pytorch中找到训练的准确性

import torch
import torch.nn as nn
import torch.optim as optim

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc = nn.Linear(10, 2)  # 示例中使用一个全连接层

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

# 加载数据
train_data = torch.randn(100, 10)  # 示例中使用随机生成的数据
train_labels = torch.randint(0, 2, (100,))  # 示例中使用随机生成的标签

# 创建模型实例
model = Net()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 训练模型
for epoch in range(10):
    optimizer.zero_grad()
    outputs = model(train_data)
    loss = criterion(outputs, train_labels)
    loss.backward()
    optimizer.step()

# 计算准确性
test_data = torch.randn(50, 10)  # 示例中使用随机生成的测试数据
test_labels = torch.randint(0, 2, (50,))  # 示例中使用随机生成的测试标签

with torch.no_grad():
    test_outputs = model(test_data)
    _, predicted = torch.max(test_outputs, 1)
    accuracy = (predicted == test_labels).sum().item() / len(test_labels)

print("准确性：", accuracy)