分层差异化学习率
以不同方式训练深层与浅层
分层差异化学习率 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。
One Rate for All?
So far every layer shared a single learning rate. But early and late layers learn very different things. Maybe they deserve different rates. 🎚️
Early Layers Are General
The early layers of a pretrained net detect edges, colors, and textures. These features transfer to almost any task, so they barely need changing.
Late Layers Are Specific
The later layers capture task-specific patterns. These are the ones that should adapt most to your new dataset.
The Big Idea
Discriminative fine-tuning gives deep layers a tiny learning rate and shallow output layers a larger one. Each part learns at the right speed.
Group the Parameters
To use different rates, you split the model into parameter groups, each with its own settings.
backbone_params = model.features.parameters()
head_params = model.classifier.parameters()Per-Group Learning Rates
PyTorch optimizers accept a list of dicts, one per group, each carrying its own lr. The backbone gets a small one, the head a bigger one.
opt = torch.optim.Adam([
{'params': backbone_params, 'lr': 1e-5},
{'params': head_params, 'lr': 1e-3}])Why It Helps
This protects the precious general features while letting the head adapt quickly. You often get higher accuracy than a single shared rate.
Slanted Across Many Layers
You can scale rates smoothly: each deeper block gets a slightly smaller rate. This gradient of speeds is sometimes called a slanted schedule.
Gradual Unfreezing
A partner technique is gradual unfreezing: unfreeze the top layer first, train, then unfreeze the next, working downward over epochs.
Inspect Your Groups
You can read back each group's settings from the optimizer to confirm every param_group got the rate you intended.
for g in opt.param_groups:
print(g['lr'])When to Reach for It
Discriminative rates pay off most on deep networks where the gap between general and specific layers is large.
Quick Check
In discriminative fine-tuning, which layers should get the smallest learning rate?
Recap
You gave deep layers small rates and the head a larger one using parameter groups, optionally with gradual unfreezing, for sharper transfer learning. 🎯
常见问题解答
「分层差异化学习率」课时是免费的吗？
是的 — 「分层差异化学习率」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。
「分层差异化学习率」这节课中我会学到什么？
以不同方式训练深层与浅层 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。
「分层差异化学习率」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。