Pytorch线性模型实现——up主：刘二大人《PyTorch深度学习实践》

教程： https://www.bilibili.com/video/BV1Y7411d7Ys?p=2&vd_source=715b347a0d6cb8aa3822e5a102f366fe
数据集：
$$\begin{gathered} x_{data}=[1.0,2.0,3.0] \\ {y}_{data}=[2.0,4.0,6.0] \end{gathered}$$
参数：
$$\begin{gathered} w_{list}=[0.0,4.0,0.1] \\ {b}_{list}=[-2.0,2.1,0.1] \end{gathered}$$
模型：
$$y=w\ast x+b$$

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]


def forward(x, y, w, b):
  #矩阵计算 x为标量；w，b为矩阵
  return x * w + b

#定义损失函数
def loss(y_pred, y):
  return (y_pred - y) **2

w_list = np.arange(0.0, 4.0, 0.1)
b_list = np.arange(-2.0, 2.1, 0.1)

w, b = np.meshgrid(w_list, b_list)
mse = np.zeros(w.shape)
for x,y in zip(x_data, y_data):
  y_pred = forward(x, y, w, b)
  mse += loss(y_pred, y)
mse /= len(x_data)

fig = plt.figure()
ax = fig.add_subplot(projection='3d')
plt.xlabel(r'w', fontsize=20, color='cyan')
plt.ylabel(r'b', fontsize=20, color='cyan')
ax.plot_surface(w, b, mse, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow'))
plt.show()