PyTorch 基础教程

PyTorch 是一个流行的开源机器学习库，它提供了灵活的深度学习框架。以下是一些 PyTorch 的基础概念和教程。

安装 PyTorch

首先，您需要安装 PyTorch。您可以通过以下命令进行安装：

pip install torch torchvision

快速开始

导入 PyTorch 库
```
import torch
```
创建一个张量
```
tensor = torch.tensor([1, 2, 3])
```
查看张量的形状
```
print(tensor.shape)
```

计算张量的梯度

tensor.backward(torch.tensor([1.0, 1.0, 1.0]))

图像分类

图像分类是 PyTorch 中一个常见的应用场景。以下是一个简单的图像分类示例：

import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader

# 定义一个简单的卷积神经网络
class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2))
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 加载数据集
transform = transforms.Compose([transforms.ToTensor()])
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)

# 创建模型、损失函数和优化器
model = SimpleCNN()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# 训练模型
for epoch in range(2):  # loop over the dataset multiple times
    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 every 100 mini-batches
            print(f'[{epoch + 1}, {i + 1}] loss: {running_loss / 100:.3f}')
            running_loss = 0.0

print('Finished Training')

扩展阅读

如果您想了解更多关于 PyTorch 的内容，请访问我们的PyTorch 教程页面。