Python中如何用numpy解决梯度下降最小值

<p style="line-height: 1.75em;"><span style="font-family: 微软雅黑, "Microsoft YaHei"; font-size: 14px;">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()<br></span></p>

<p style="line-height: 1.75em;"><span style="font-family: 微软雅黑, "Microsoft YaHei"; font-size: 14px;">X0 [1.93745582]

0 steps,x_ =  [1.93558091]

100 steps,x_ =  [1.76583547]

200 steps,x_ =  [1.6268875]

300 steps,x_ =  [1.51314929]

400 steps,x_ =  [1.42004698]

500 steps,x_ =  [1.34383651]

600 steps,x_ =  [1.28145316]

700 steps,x_ =  [1.23038821]

800 steps,x_ =  [1.18858814]

900 steps,x_ =  [1.15437199]

1000 steps,x_ =  [1.12636379]

1100 steps,x_ =  [1.1034372]

1200 steps,x_ =  [1.08467026]

1300 steps,x_ =  [1.06930826]

1400 steps,x_ =  [1.05673344]

1500 steps,x_ =  [1.04644011]

1600 steps,x_ =  [1.03801434]

1700 steps,x_ =  [1.03111727]

1800 steps,x_ =  [1.02547157]

1900 steps,x_ =  [1.02085018]<br></span></p>