归一化与标准化输入
缩放特征，使训练能够收敛
归一化与标准化输入 是 CoddyKit 上的免费 Deep Learning Academy 课时。 这是第 4 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。
Raw Numbers Slow Learning
Features on wildly different scales, like age and salary, confuse a network. Rescaling them first is why normalization makes training converge faster. ⚖️
Two Common Recipes
You will meet two main scalers: standardization shifts data to mean zero and unit variance, while min-max squeezes it into a fixed range like 0 to 1.
Standardize with Mean and Std
Standardization subtracts the mean and divides by the standard deviation. The result is centered on zero with a spread of one, the deep learning default.
x = (x - x.mean()) / x.std()Min-Max Scaling
Min-max scaling maps your smallest value to 0 and largest to 1. It is handy when a bounded input range matters, like pixel intensities.
x = (x - x.min()) / (x.max() - x.min())Fit on Train Only
Compute the mean and std from the training set only. Reusing test data to set these stats leaks information and inflates your scores.
Reuse the Same Stats
Apply the exact training statistics to validation and test data. Both splits must be transformed the same way or your model sees mismatched inputs.
x_val = (x_val - train_mean) / train_stdPer-Channel for Images
For images you normalize each color channel with its own mean and std. PyTorch ships transforms.Normalize to do exactly this in a pipeline.
transforms.Normalize(mean=[0.5], std=[0.5])Borrow Known ImageNet Stats
When using pretrained vision models, normalize with the ImageNet means and stds they were trained on. Matching those numbers keeps inputs in the expected range.
Do It Inside __getitem__
The cleanest spot to scale is your dataset's __getitem__ or a transform. Then every sample arrives normalized without extra code in your training loop.
Watch for Divide by Zero
If a feature is constant its std is zero and dividing explodes. Add a tiny epsilon to the denominator to keep the math safe and stable.
x = (x - mean) / (std + 1e-8)Small Step, Big Payoff
Normalizing inputs is a quick preprocessing habit, yet it often unlocks faster, more stable training. Skip it and even a good model may crawl.
Quick Check
From which split should you compute normalization statistics?
Recap
Normalization rescales features so training converges, using stats fit on the training set and reused everywhere, often inside a transform. 🎉
常见问题解答
「归一化与标准化输入」课时是免费的吗？
是的 — 「归一化与标准化输入」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。
「归一化与标准化输入」这节课中我会学到什么？
缩放特征，使训练能够收敛 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 4 节课，共 4 节。
「归一化与标准化输入」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。