python梯度下降法

用numpy实现梯度下降法，参考一下这篇文章：
https://www.cnblogs.com/noluye/p/11108513.html

import numpy as np 
import matplotlib.pyplot as plt


def get_loss(x):
    c,r = x.shape
    loss = (x**2 - 2*x + 3) + (0.01*(2*np.random.rand(c,r)-1))
    return(loss)

x = np.arange(0,3,0.01).reshape(-1,1)


"""plt.title("loss")
plt.plot(get_loss(np.array(x)))
plt.show()"""


def get_grad(x):
    grad = 2 * x -2
    return(grad)

np.random.seed(31415)
x_ = np.random.rand(1)*3
x_s = []
alp = 0.001
print("X0",x_)
for e in range(2000):

    x_ = x_ - alp*(get_grad(x_))
    x_s.append(x_)
    if(e%100 == 0):
        print(e,"steps,x_ = ",x_)

plt.title("loss")
plt.plot(get_loss(np.array(x_s)))
plt.show()