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本帖最后由 雷达 于 2023-2-14 13:12 编辑
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: P4 {9 A7 Q s8 @为预防老年痴呆,时不时学点新东东玩一玩。9 Z3 @- G; v- O9 y1 m, v6 j
Pytorch 下面的代码做最简单的一元线性回归:
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import torch# S, R$ ~. L0 s# E
import numpy as np
4 F1 j) y1 z7 V6 K& Cimport matplotlib.pyplot as plt
( s4 P) [7 Q( c& i zimport random% w! R# s" h8 E0 x/ ?% w
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x = torch.tensor(np.arange(1,100,1))
0 ] l2 F7 |1 W( |. d& Vy = (x*27+15+random.randint(-2,3)).reshape(-1) # y=wx+b, 真实的w0 =27, b0=15
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w = torch.tensor(0.,requires_grad=True) #设置随机初始 w,b
* H: l9 _/ ~3 v8 W4 db = torch.tensor(0.,requires_grad=True)" Y1 u5 F+ t6 ?3 V t {
6 v# n! H, n. Q# t4 |epochs = 100
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losses = []7 b" Y9 } Z5 w/ w0 Y' d" U
for i in range(epochs):
2 q; v+ [/ ^4 V5 D: Z1 D/ R W J y_pred = (x*w+b) # 预测
h$ l( V7 E7 o3 U" R0 \ y_pred.reshape(-1), O! |* N% \% j4 i5 ?+ d* N
X4 K9 Z4 U, |1 @ loss = torch.square(y_pred - y).mean() #计算 loss
$ e" d4 u5 @4 g* O losses.append(loss)
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* }) w: A! y+ ?2 d loss.backward() # autograd* e4 h9 t, m" m& ?+ U! U
with torch.no_grad():6 L9 |1 ~6 c. I' a6 q* ^9 z
w -= w.grad*0.0001 # 回归 w# l' _% _7 A' E* S0 x
b -= b.grad*0.0001 # 回归 b . F( h0 g0 ]; l
w.grad.zero_()
: h: v9 a6 ?4 R% A( {. V b.grad.zero_()" F' I2 B' e2 c% X: H
9 y, ] F3 c# q- e+ L( W, y+ j9 Q5 Xprint(w.item(),b.item()) #结果; @ m2 N a" d2 Q
$ p* V. ] Z6 G4 gOutput: 27.26387596130371 0.4974517822265625
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! D' N0 f, A! F% {+ e最后的结果,w可以回到 w0 = 27 附近,b却回不去 b0=15。两处红字,损失函数是矢量计算后的均值,感觉 b 的回归表达有问题。5 W9 ?3 z+ \# q; H$ a4 `1 v
高手们帮看看是神马原因?
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