pytorch训练代码介绍

好的，以下是一个简单的PyTorch训练代码的介绍：

1. 导入必要的库和模块：

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

2. 定义数据集和数据加载器：

train_dataset = ...
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)

3. 定义模型：

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc1 = nn.Linear(784, 256)
        self.fc2 = nn.Linear(256, 10)

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

model = MyModel()

4. 定义损失函数和优化器：

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

5. 训练模型：

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

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

6. 测试模型：

test_dataset = ...
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False)

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

print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))

以上就是一个简单的PyTorch训练代码的介绍，其中还有很多细节需要根据具体情况进行调整。