tensorflow使用逆矩阵实现线性回归

最新推荐文章于 2024-12-05 00:00:00 发布

转载最新推荐文章于 2024-12-05 00:00:00 发布 · 165 阅读

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原文链接：https://my.oschina.net/u/2511906/blog/2223213

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#数据结构与算法 #python

本文通过Python和TensorFlow实现线性回归分析，利用最小二乘法拟合最佳拟合线，展示如何从随机噪声数据中提取线性关系，并使用matplotlib可视化数据点和回归线。

2019独角兽企业重金招聘Python工程师标准>>>

import matplotlib.pyplot as plt import numpy as np import tensorflow as tf

sess = tf.Session() x_vals = np.linspace(0,10,100) y_vals = x_vals+np.random.normal(0,1,100) x_vals_column = np.transpose(np.matrix(x_vals)) one_clumn = np.transpose(np.matrix(np.repeat(1,100))) A = np.column_stack((x_vals_column,one_clumn)) b =np.transpose(np.matrix(y_vals))

A_tensor = tf.constant(A) b_tensor = tf.constant(b)

tA_A = tf.matmul(tf.transpose(A_tensor),A_tensor)

tA_A_inv = tf.matrix_inverse(tA_A) product = tf.matmul(tA_A_inv,tf.transpose(A_tensor)) solution = tf.matmul(product,b_tensor)

solution_eval = sess.run(solution) print(solution_eval)

slope = solution_eval[0][0] y_intercept = solution_eval[1][0]

best_fit =[] for i in x_vals: best_fit.append(slope*i+y_intercept) plt.plot(x_vals,y_vals,'o',label = 'data') plt.plot(x_vals,best_fit,'r--',label='best fit line') plt.legend(loc = 'upper left') plt.show()

转载于:https://my.oschina.net/u/2511906/blog/2223213