1 # -*- coding: utf-8 -*- 2 """ 3 Created on Tue Oct 2 15:49:08 2018 4 5 @author: zhen 6 """ 7 8 import tensorflow as tf 9 import numpy as np 10 from sklearn.datasets import fetch_california_housing 11 12 x = tf.Variable(3, name='x') 13 y = tf.Variable(4, name='y') 14 15 # 任何创建的节点会自动加入到默认的图中 16 print(x.graph is tf.get_default_graph()) 17 18 # 创建新的图 19 graph = tf.Graph() 20 21 with graph.as_default(): 22 # 只在with范围内有效 23 demo = tf.Variable(3) 24 25 print(demo.graph is graph) 26 27 demo2 = tf.Variable(3) 28 print(demo2.graph is graph) 29 30 # 创建常量 31 constant = tf.constant(3) 32 33 f = x * x * y + x * y + 1 34 f2 = f * constant 35 36 # 可以不分别对每个变量去进行初始化,在run运行时初始化 37 init = tf.global_variables_initializer() 38 39 with tf.Session() as sess: 40 init.run() 41 result = f.eval() 42 result2 = f2.eval() 43 print(result, result2) 44 45 f_result, f2_result = sess.run([f, f2]) 46 print(f_result, f2_result) 47 48 # 获取数据集 49 housing = fetch_california_housing(data_home="C:/Users/zhen/.spyder-py3/data", download_if_missing=True) 50 # 获取x数据行数和列数 51 m, n = housing.data.shape 52 # 添加额外数据加入特征 53 housing_data_plus_bias = np.c_[np.ones((m, 1)), housing.data] 54 # 创建两个Tensorflow常量节点x和y,去持有数据和标签 55 x = tf.constant(housing_data_plus_bias, dtype=tf.float32, name='x') 56 y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name='y') 57 # 矩阵操作 58 xt = tf.transpose(x) 59 # 计算最优解 60 theta = tf.matmul(tf.matmul(tf.matrix_inverse(tf.matmul(xt, x)), xt), y) 61 with tf.Session() as sess: 62 theta_value = theta.eval() 63 print(theta_value)
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