[LAB 10 · 从 RGB 图像训练小型检测器](/course/notebooks/a14237bfa9dd413d959ab4e458e11f42.html)

# 选读热身：四维分类的参数更新 · 实现代码
[独立阅读](/course/notebook-blocks/bec8290eec6f4d2099698e2e9a5971f7.html)
主线阅读与优先实验

RGB图像、单特征层锚框检测器、分类/位置损失、NMS、独立检测评价
展开完整实现代码
```
import numpy as np
rng=np.random.default_rng(7)
X=rng.normal(size=(64,4)); true=np.array([.8,-.4,.3,.2]); y=(X@true>0).astype(float);w=np.zeros(4)
for step in range(200):
    p=1/(1+np.exp(-X@w));w-=.2*(X.T@(p-y))/len(y)
print('four-dimensional logistic warm-up, training accuracy:',float(((X@w>0)==y).mean()))
print('learned weights:',w.round(3))
# A fresh held-out matrix answers a different question from training accuracy.
held=np.random.default_rng(107).normal(size=(256,4));held_y=held@true>0
print('same-rule held-out accuracy:',float(((held@w>0)==held_y).mean()))
shuffled=np.random.default_rng(17).permutation(y);ws=np.zeros(4)
for step in range(200):
    ps=1/(1+np.exp(-X@ws));ws-=.2*(X.T@(ps-shuffled))/len(shuffled)
print('only training labels shuffled, held-out true-rule accuracy:',float(((held@ws>0)==held_y).mean()))
```