《动手学深度学习》PyTorch版教程

《动手学深度学习》PyTorch版教程

项目地址:https://gitcode.com/gh_mirrors/di/Dive-Into-Deep-Learning-PyTorch-PDF

项目介绍

本项目对中文版《动手学深度学习》（第一版）中的代码进行了PyTorch实现，并整理为PDF版本供下载。项目旨在帮助读者通过PyTorch框架学习和实践深度学习技术。除了原书内容外，本项目还增加了一些额外的附录和实现，如语义分割网络（U-Net）的实现，以增强学习体验。

项目快速启动

环境配置

首先，确保你已经安装了Python和PyTorch。你可以通过以下命令安装PyTorch：

pip install torch torchvision

下载项目

你可以通过以下命令克隆项目到本地：

git clone https://github.com/wzy6642/Dive-Into-Deep-Learning-PyTorch-PDF.git

运行示例代码

进入项目目录并运行示例代码：

cd Dive-Into-Deep-Learning-PyTorch-PDF
python examples/linear_regression.py

应用案例和最佳实践

线性回归

线性回归是深度学习中的基础模型之一。以下是一个简单的线性回归实现示例：

import torch
import torch.nn as nn
import numpy as np

# 生成数据
np.random.seed(0)
x = np.random.rand(100, 1)
y = 1 + 2 * x + 0.1 * np.random.randn(100, 1)

# 转换为Tensor
x_tensor = torch.from_numpy(x).float()
y_tensor = torch.from_numpy(y).float()

# 定义模型
class LinearRegression(nn.Module):
    def __init__(self):
        super(LinearRegression, self).__init__()
        self.linear = nn.Linear(1, 1)

    def forward(self, x):
        return self.linear(x)

model = LinearRegression()
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# 训练模型
for epoch in range(100):
    model.train()
    optimizer.zero_grad()
    outputs = model(x_tensor)
    loss = criterion(outputs, y_tensor)
    loss.backward()
    optimizer.step()

    if (epoch+1) % 10 == 0:
        print(f'Epoch [{epoch+1}/100], Loss: {loss.item():.4f}')

语义分割

语义分割是深度学习在计算机视觉中的一个重要应用。以下是一个简单的U-Net实现示例：

import torch
import torch.nn as nn

class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(DoubleConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
        )

    def forward(self, x):
        return self.conv(x)

class UNet(nn.Module):
    def __init__(self, in_channels=1, out_channels=1, features=[64, 128, 256, 512]):
        super(UNet, self).__init__()
        self.ups = nn.ModuleList()
        self.downs = nn.ModuleList()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

        # Down part
        for feature in features:
            self.downs.append(DoubleConv(in_channels, feature))
            in_channels = feature

        # Up part
        for feature in reversed(features):
            self.ups.append(

Dive-Into-Deep-Learning-PyTorch-PDF 项目地址: https://gitcode.com/gh_mirrors/di/Dive-Into-Deep-Learning-PyTorch-PDF