PyTorch 基础教程

PyTorch 是一个流行的开源机器学习库，它使得深度学习变得更加容易。以下是一些PyTorch基础教程，帮助您开始学习。

入门指南

安装PyTorch
熟悉Python基础
了解深度学习的基本概念

基础操作

torch.Tensor：张量操作
torch.nn.Module：定义神经网络模型
torch.optim：优化器
torch.utils.data：数据加载

示例代码

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

# 创建一个简单的神经网络
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 3)
        self.conv2 = nn.Conv2d(6, 16, 3)
        self.fc1 = nn.Linear(16 * 6 * 6, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = torch.max_pool2d(x, (2, 2))
        x = torch.relu(self.conv2(x))
        x = torch.max_pool2d(x, 2)
        x = x.view(-1, self.num_flat_features(x))
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]  # 除批量大小外的所有维度
        num_features = 1
        for s in size:
            num_features *= s
        return num_features

net = Net()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练模型
for epoch in range(2):  # 我们只需要训练两个周期
    optimizer.zero_grad()
    output = net(input)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()

print('Finished Training')

资源链接