PyTorch常见的代码实现

PyTorch是一个基于Python的科学计算库，用于构建深度学习模型。以下是PyTorch中常见的代码实现：

1. 张量创建和操作

import torch

# 创建一个张量
x = torch.tensor([1, 2, 3])

# 创建一个随机张量
y = torch.randn(3, 3)

# 张量加法
z = x + y

# 张量乘法
w = x.matmul(y)

# 改变张量形状
z = z.view(9)

2. 模型定义和训练

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

# 定义一个网络
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(10, 5)
        self.fc2 = nn.Linear(5, 1)

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

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

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('[%d] loss: %.3f' % (epoch + 1, running_loss / len(trainloader)))

3. 数据加载和预处理

import torch.utils.data as data
from torchvision import datasets, transforms

# 定义一个数据集
transform = transforms.Compose([transforms.ToTensor(), 
                                transforms.Normalize((0.5,), (0.5,))])
trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = data.DataLoader(trainset, batch_size=32, shuffle=True)

# 加载数据集
for i, data in enumerate(trainloader, 0):
    inputs, labels = data

这些是PyTorch中常见的代码实现，它们涵盖了PyTorch的大部分功能，但并非全部。