给出一般pytorch实现训练神经网络格式

以下是一般pytorch实现训练神经网络的格式：

1. 导入相关库和数据集

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset

# 导入数据集
train_data = YourTrainDataset()
valid_data = YourValidDataset()
test_data = YourTestDataset()

# 创建数据加载器
train_loader = DataLoader(train_data, batch_size=32, shuffle=True)
valid_loader = DataLoader(valid_data, batch_size=32)
test_loader = DataLoader(test_data, batch_size=32)

2. 定义网络模型

class YourNet(nn.Module):
    def __init__(self):
        super(YourNet, self).__init__()
        self.fc1 = nn.Linear(784, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = x.view(-1, 784)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

net = YourNet()

3. 定义损失函数和优化器

# 定义损失函数
criterion = nn.CrossEntropyLoss()

# 定义优化器
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

4. 训练网络

for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data

        # 将参数梯度清零
        optimizer.zero_grad()

        # 前向传播、计算损失、反向传播、更新参数
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # 统计损失
        running_loss += loss.item()

    # 计算平均损失并输出
    train_loss = running_loss / len(train_loader)
    print('Epoch %d, training loss: %.3f' % (epoch+1, train_loss))

5. 测试网络

correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy on test set: %.2f%%' % (100 * correct / total))

6. 保存和加载模型

# 保存模型
torch.save(net.state_dict(), 'your_net.pth')

# 加载模型
net = YourNet()
net.load_state_dict(torch.load('your_net.pth'))