tensorflow实现线性回归（矩阵解法）

import matplotlib.pyplot as plt

import numpy as np
import tensorflow as tf
sess = tf.Session()
x_vals = np.linspace(0,10,100) # [0,10]之间产生100个等差数值
y_vals = x_vals + np.random.normal(0,1,100) # 正态分布，产生100 个数值

x_vals_column = np.transpose(np.matrix(x_vals)) #将x的值转换为一列

ones_column = np.transpose(np.matrix(np.repeat(1,100))) #生成一列元素全为1的矩阵，共100个元素
A = np.column_stack((x_vals_column,ones_column)) #将x值和上面的组合成一个新的矩阵[100,2]
b = np.transpose(np.matrix(y_vals))

A_tensor = tf.constant(A) #将矩阵转化为tensorflow的张量
b_tensor = tf.constant(b)

tA_A = tf.matmul(tf.transpose(A_tensor),A_tensor) #将A 的转置和A做矩阵乘法
tA_A_inv = tf.matrix_inverse(tA_A) #求逆矩阵
product = tf.matmul(tA_A_inv,tf.transpose(A_tensor))
solution = tf.matmul(product,b_tensor)
solution_eval = sess.run(solution) #求解系数矩阵
slope = solution_eval[0][0]
y_intercept = solution_eval[1][0]
print(‘slope: ‘,str(slope))
print(‘y_intercept: ‘,str(y_intercept))

best_fit = []
for i in x_vals:
best_fit.append(slope*i + y_intercept)
plt.plot(x_vals,y_vals,’o’,color = ‘g’,label=’Data’)
plt.plot(x_vals,best_fit,’r-‘,label=’Best fit line’,linewidth = 4)
plt.legend(loc=’upper left’)
plt.show()