PyTorch 教程代码示例

以下是 PyTorch 教程中的代码示例，可以帮助你更好地理解 PyTorch 的使用。

1. 简单神经网络

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

# 定义一个简单的神经网络
class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(10, 50)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(50, 1)

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

# 实例化网络和优化器
model = SimpleNN()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 训练模型
for epoch in range(100):
    optimizer.zero_grad()
    output = model(torch.randn(1, 10))
    loss = (output - 1)**2
    loss.backward()
    optimizer.step()

    if epoch % 10 == 0:
        print(f'Epoch {epoch}, Loss: {loss.item()}')

2. 图像分类

import torch
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch.nn as nn
import torch.optim as optim

# 加载数据集
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
])

train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

# 定义模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(32 * 28 * 28, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = x.view(-1, 32 * 28 * 28)
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 实例化网络和优化器
model = CNN()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(10):
    for data, target in train_loader:
        optimizer.zero_grad()
        output = model(data)
        loss = nn.CrossEntropyLoss()(output, target)
        loss.backward()
        optimizer.step()

    if epoch % 1 == 0:
        print(f'Epoch {epoch}, Loss: {loss.item()}')

更多 PyTorch 教程代码示例，请访问PyTorch 教程。

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