用 Softmax 计算概率
将原始分数转换为类别分布
用 Softmax 计算概率 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。
From Scores to Choices
A classifier's last layer spits out raw scores, one per class. These unbounded numbers are called logits, and they are hard to read directly. 🔢
Enter Softmax
Softmax turns a vector of logits into a clean probability distribution over all your classes. Now each score becomes a chance.
import torch.nn.functional as F
probs = F.softmax(logits, dim=-1)Two Guarantees
Softmax promises two things: every output is between 0 and 1, and all of them sum to one. That is exactly what a distribution needs.
How It Works
Softmax exponentiates each logit, then divides by the total. The exponential makes bigger logits dominate, sharpening the winner.
exp = logits.exp()
probs = exp / exp.sum()Relative, Not Absolute
Softmax cares about the gaps between logits, not their raw size. Adding the same constant to every logit leaves the probabilities unchanged.
Sigmoid vs Softmax
Use sigmoid for one yes-or-no output. Use softmax when classes compete and exactly one of several should win.
Picking the Winner
To get the predicted class, take the argmax of the softmax output. That index is the class with the highest probability.
pred = probs.argmax(dim=-1)Mind the Dimension
Always set the right dim so softmax normalizes across classes, not across the batch. A wrong axis quietly ruins your probabilities.
Skip It While Training
During training you usually do not apply softmax yourself. The cross-entropy loss expects raw logits and applies it internally.
loss = F.cross_entropy(logits, target) # logits, not probsWhy That Is Safer
Combining softmax and the log inside one function is more numerically stable. Doing both steps by hand can overflow or lose precision.
Temperature Tweaks Sharpness
Dividing logits by a temperature before softmax controls confidence. Low values sharpen the peak; high values flatten it out.
Quick Check
Think about what cross-entropy loss expects as its input.
Recap
Softmax converts logits into probabilities that sum to one, perfect for multiclass output. But let cross-entropy handle it during training. 🏁
常见问题解答
「用 Softmax 计算概率」课时是免费的吗？
是的 — 「用 Softmax 计算概率」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。
「用 Softmax 计算概率」这节课中我会学到什么？
将原始分数转换为类别分布 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。
「用 Softmax 计算概率」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。