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笔记本、脚本与可复现的随机种子

设置随机种子，让结果可以重复

笔记本、脚本与可复现的随机种子 是 CoddyKit 上的免费 Deep Learning Academy 课时。 这是第 3 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Deep Learning Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Deep Learning Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Two Ways to Work

You will write code in notebooks for quick experiments and in plain scripts for repeatable runs. Each suits a different moment.

Notebooks for Exploring

A notebook runs code in cells, showing plots and results inline. It is perfect for poking at data and trying ideas fast. 📓

Scripts for Real Runs

A script runs top to bottom in one go. Use it for training jobs you want to launch, share, and reproduce exactly.

python train.py

Watch Out for Cell Order

Notebook cells keep state even when you run them out of order, which can hide bugs. A script always starts from a clean slate.

The Reproducibility Problem

Deep learning uses random numbers for weights and shuffling. Run twice and you get different results unless you control that randomness.

Seeds to the Rescue

A seed fixes the starting point of the random number generator, so the same code produces the same numbers every single run.

Seed PyTorch

Set PyTorch's generator with one call. manual_seed makes weight initialization and any torch randomness repeatable.

torch.manual_seed(42)

Don't Forget NumPy and Python

Your code often pulls randomness from NumPy and Python too, so seed all three to make a run fully reproducible.

import numpy as np, random
np.random.seed(42)
random.seed(42)

Seed the GPU Too

When you train on CUDA, also seed the GPU generators. cuda.manual_seed_all covers every GPU device in your machine.

torch.cuda.manual_seed_all(42)

Wrap It in a Function

Bundle every seed call into one set_seed helper. Call it at the top of each run so you never forget a source of randomness.

def set_seed(s):
    torch.manual_seed(s)
    np.random.seed(s)

Why It Matters

Reproducible runs let you compare experiments fairly. If accuracy improves, you know it was your change, not random luck.

Quick Check

Recall what makes a run repeatable.

Recap

You saw when to use notebooks versus scripts and how seeding torch, NumPy, and Python makes every run reproducible. 🌱

常见问题解答

「笔记本、脚本与可复现的随机种子」课时是免费的吗？

是的 — 「笔记本、脚本与可复现的随机种子」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Deep Learning Academy 课程的其余内容，请升级到 CoddyKit PRO。 Deep Learning Academy 课程共包含 4 节课。

「笔记本、脚本与可复现的随机种子」这节课中我会学到什么？

设置随机种子，让结果可以重复 你通过在浏览器中直接运行的动手代码来练习 Deep Learning Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Deep Learning Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Deep Learning Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 3 节课，共 4 节。

「笔记本、脚本与可复现的随机种子」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Deep Learning Academy 课中编写并运行代码吗？

能。每节 Deep Learning Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

  1. 安装 PyTorch 并验证导入
  2. CPU、GPU 与 MPS：选择设备
  3. 笔记本、脚本与可复现的随机种子
  4. 您的第一个 torch.tensor
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