深度学习-线性回归与平方损失

# import torch
import math
# import time
import numpy as np
from d2l import torch as d2ldef normal(x, mu, sigma):p = 1 / math.sqrt(2 * math.pi * sigma**2) return p * np.exp(-0.5 / sigma**2 * (x - mu)**2)
# 再次使⽤numpy进⾏可视化 
x = np.arange(-7, 7, 0.01)
# 均值和标准差对 
params = [(0, 1), (0, 2), (3, 1)] 
d2l.plot(x, [normal(x, mu, sigma) for mu, sigma in params], xlabel='x', ylabel='p(x)', figsize=(4.5, 2.5), legend=[f'mean {mu}, std {sigma}' for mu, sigma in params])
d2l.plt.show()