TensorFlow使用之实现线性回归

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

# 立刻下载数据集
housing = fetch_california_housing(data_home="C:/Users/28542/scikit_learn_data", download_if_missing=False)
# 获得X数据行数和列数
m, n = housing.data.shape
# 这里添加一个额外的bias输入特征(x0=1)到所有的训练数据上面，因为使用的numpy所有会立即执行
housing_data_plus_bias = np.c_[np.ones((m, 1)), housing.data]
# 创建两个TensorFlow常量节点X和y，去持有数据和标签
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')
# 使用一些TensorFlow框架提供的矩阵操作去求theta
XT = tf.transpose(X)
# 解析解一步计算出最优解
theta = tf.matmul(tf.matmul(tf.matrix_inverse(tf.matmul(XT, X)), XT), y)
with tf.Session() as sess:
    theta_value = theta.eval()  # sess.run(theta)
    print(theta_value)