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 = 0w0 = 0for 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_gradientreturn 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 = 0w0 = 0for 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_gradientreturn 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)