Tensorflow Practice 2-4

 1 import tensorflow as tf
 2 import numpy as np
 3 
 4 '''使用numpy生成100个随机点'''
 5 x_data = np.random.rand(100)
 6 y_data = x_data*0.1 + 0.2
 7 
 8 '''构造一个线性模型'''
 9 b = tf.Variable(0.)
10 k = tf.Variable(0.)
11 y = k*x_data + b
12 
13 '''二次代价函数'''
14 loss = tf.reduce_mean(tf.square(y_data - y))
15 '''定义一个梯度下降法来进行训练的优化器'''
16 optimizer = tf.train.GradientDescentOptimizer(0.2)
17 '''最小化代价函数'''
18 train = optimizer.minimize(loss)
19 
20 '''初始化变量'''
21 init = tf.global_variables_initializer()
22 
23 with tf.Session() as sess:
24     sess.run(init)
25     for step in range(201):
26         sess.run(train)
27         if step % 20 == 0:
28             print(step, sess.run([k, b]))