import tensorflow as tf
import numpy as np
from sklearn.datasets import fetch_california_housing

housing = fetch_california_housing()
m, n = housing.data.shape
housing_data_plus_bias = np.c_[np.ones((m, 1)), housing.data]

X = tf.constant(housing_data_plus_bias, dtype = tf.float32, name = 'X')
y = tf.constant(housing.target.reshape(-1, 1), dtype = tf.float32, name = 'y')
XT = tf.transpose(X)
theta = tf.matmul(tf.matmul(tf.matrix_inverse(tf.matmul(XT, X)), XT), y)

with tf.Session() as sess:
	theta_val = theta.eval()

print(theta_val)