代码如下:
1 #Linear Regression 2 import numpy as np 3 import tensorflow as tf 4 from sklearn.datasets import fetch_california_housing 5 6 7 housing = fetch_california_housing() 8 m, n = housing.data.shape 9 housing_data_plus_bias = np.c_[np.ones((m, 1)), housing.data]#merge 10 11 X = tf.constant(housing_data_plus_bias, dtype = tf.float32, name = 'X') 12 y = tf.constant(housing.target.reshape(-1,1), dtype = tf.float32, name = 'y') 13 XT = tf.transpose(X) 14 theta = tf.matmul(tf.matmul(tf.matrix_inverse(tf.matmul(XT, X)), XT), y) 15 16 with tf.Session() as sess: 17 theta_value = theta.eval()
公式是这个样子的: