关于机器学习:课程作业经验基于MindSpore的梯度下降实验

基于 mindspore 的梯度降落试验基于机器学习实际课程实现的相干应用 mindspore 深度学习框架实现的工作，写一些分享心得。数据筹备应用 numpy 生成数据，之后应用 mindspore 的 tensor 进行转换：x = np.array([55,71,68,87,101,87,75,78,93,73])
y = np.array([91,101,87,109,129,98,95,101,104,93])

x = Tensor(x.astype(np.float32))
y = Tensor(y.astype(np.float32))
复制解析解 def ols_algebra(x, y):

'''解析解'''
n = len(x)
w1 = (n*sum(x*y) - sum(x)*sum(y)) / (n*sum(x*x) - sum(x)*sum(x))
w0 = (sum(x*x)*sum(y) - sum(x)*sum(x*y)) / (n*sum(x*x) - sum(x)*sum(x))

return w1,w0

复制梯度降落解 def ols_gradient_descent(x,y,lr,num_iter):

'''梯度降落解'''
w1 = 0
w0 = 0
for i in range(num_iter):
    y_hat = (w1 * x)+ w0
    w1_gradient = -2 * sum(x*(y-y_hat))
    w0_gradient = -2*sum(y-y_hat)
    w1 -=lr * w1_gradient
    w0 -= lr* w0_gradient
return w1,w0

复制画图进行比拟 def plot_pic(w1,w0,w1_,w0_,x,y):

'''画图'''
fig, axes = plt.subplots(1,2, figsize=(15,5))
w1 = w1.asnumpy()
w0 = w0.asnumpy()
w1_ = w1_.asnumpy()
w0_ = w0_.asnumpy()
x = x.asnumpy()
y = y.asnumpy()

axes[0].scatter(x,y)
axes[0].plot(np.array([50,110]), np.array([50,110])*w1 + w0, 'r')
axes[0].set_title("OLS")

axes[1].scatter(x,y)
axes[1].plot(np.array([50,110]), np.array([50,110])*w1_ + w0_, 'r')
axes[1].set_title("Gradient descent")

plt.show()

复制后果能够看到最终梯度降落失去了和解析解极其类似的后果：

残缺代码 ”’
应用 mindspore 的 Tensor 进行批改，除画图外两头变量类型为 mindspore 的 tensor 类型
”’
import numpy as np
import matplotlib.pyplot as plt
from mindspore import Tensor

def ols_algebra(x, y):

'''解析解'''
n = len(x)
w1 = (n*sum(x*y) - sum(x)*sum(y)) / (n*sum(x*x) - sum(x)*sum(x))
w0 = (sum(x*x)*sum(y) - sum(x)*sum(x*y)) / (n*sum(x*x) - sum(x)*sum(x))

return w1,w0

def ols_gradient_descent(x,y,lr,num_iter):

'''梯度降落解'''
w1 = 0
w0 = 0
for i in range(num_iter):
    y_hat = (w1 * x)+ w0
    w1_gradient = -2 * sum(x*(y-y_hat))
    w0_gradient = -2*sum(y-y_hat)
    w1 -=lr * w1_gradient
    w0 -= lr* w0_gradient
return w1,w0

def plot_pic(w1,w0,w1_,w0_,x,y):

'''画图'''
fig, axes = plt.subplots(1,2, figsize=(15,5))
w1 = w1.asnumpy()
w0 = w0.asnumpy()
w1_ = w1_.asnumpy()
w0_ = w0_.asnumpy()
x = x.asnumpy()
y = y.asnumpy()

axes[0].scatter(x,y)
axes[0].plot(np.array([50,110]), np.array([50,110])*w1 + w0, 'r')
axes[0].set_title("OLS")

axes[1].scatter(x,y)
axes[1].plot(np.array([50,110]), np.array([50,110])*w1_ + w0_, 'r')
axes[1].set_title("Gradient descent")

plt.show()

if name == “__main__”:

x = np.array([55,71,68,87,101,87,75,78,93,73])
y = np.array([91,101,87,109,129,98,95,101,104,93])

x = Tensor(x.astype(np.float32))
y = Tensor(y.astype(np.float32))

w1,w0 = ols_algebra(x,y)
print(w1)
print(w0)

w1_,w0_ = ols_gradient_descent(x,y,lr = 0.00001, num_iter = 500)
print(w1_)
print(w0_)

plot_pic(w1,w0,w1_,w0_,x,y)

w1_,w0_ = ols_gradient_descent(x,y,lr = 0.00001, num_iter = 120000)
print(w1_)
print(w0_)

plot_pic(w1,w0,w1_,w0_,x,y)