Machine Learning Fundamentals · 免费预览

独热标签与 softmax

分类用于预测类别，类别名称本身不带有顺序，而 softmax 将一个得分向量转换为各类别上的概率分布。

1. Encoding a category

Encoding a category

2. One affine function per class

One affine function per class

3. Turning scores into probabilities

Turning scores into probabilities

4. Why raw scores cannot be probabilities

A three-class model produces outputs $\mathbf{o} = (2.0, -1.5, 4.0)$ from its affine layer. These values are passed to a cross-entropy loss without a softmax. Which statement describes the difficulty?

5. Softmax on a two-class case

A binary classifier produces scores $o_1 = 0$ and $o_2 = 0$ . The softmax assigns probability $\hat{y}_1 = \exp(o_1)/(\exp(o_1) + \exp(o_2))$ to class 1. The value of $\hat{y}_1$ is ____.

进入交互式练习

免费注册 · 即时批改 · 自动安排间隔复习。

免费开始这门课