线性模型 y=w*x

import matplotlib.pyplot as plt  
  
x_data = [1.0, 2.0, 3.0]  
y_data = [2.0, 4.0, 6.0]  
  
def forward(x):  
    return x * w  
  
def loss(x, y):  
    y_pred = forward(x)  
    return (y_pred - y) * (y_pred - y)  
  
w_list = []  
mse_list = []  
for w in np.arange(0.0, 4.1, 0.1):  
    print('w=', w)  
    l_sum = 0  
for x_val, y_val in zip(x_data, y_data):  
    y_pred_val = forward(x_val)  
    loss_val = loss(x_val, y_val)  
    l_sum += loss_val  
    print('\t', x_val, y_val, y_pred_val, loss_val)  
print('MSE=', l_sum / 3)  
w_list.append(w)  
mse_list.append(l_sum / 3)  
  
plt.plot(w_list, mse_list)  
plt.ylabel('Loss')  
plt.xlabel('w')  
plt.show()

作业：y=w*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] #输入数据  
y_data = [3.0, 5.0, 7.0]  
  
def forward(x): #定义线性回归模型  
    return x * w + b  
  
def loss(y_pred, y): #定义损失函数  
    return (y_pred - y) * (y_pred - y)  
  
w = np.arange(0.0, 4.1, 0.1)  
b = np.arange(0.0, 4.1, 0.1)  
  
[w, b] = np.meshgrid(w, b)  
  
l_sum = 0  
for x_val, y_val in zip(x_data, y_data):  
    y_pred = forward(x_val)  
    loss_val = loss(y_pred, y_val)  
    l_sum += loss_val  
    print(y_pred)  
  
fig = plt.figure()
ax = fig.add_subplot(projection = '3d')  
  
ax.plot_surface(w, b, l_sum/3)  
plt.show()