构造一个线性模型优化器

#示例
import numpy as np
import tensorflow as tf
x_data = np.random.rand(100)
y_data =  .1 * x_data +.2

## 构造一个线性模型优化器
#优化 $y = kx+b$
#待优化的参数
b = tf.Variable(0.)
k = tf.Variable(0.)
y = k * x_data + b

## 二次代价函数
loss = tf.reduce_mean(tf.square(y_data - y))
##  指定优化器
optimizer = tf.train.GradientDescentOptimizer(0.2)
train = optimizer.minimize(loss)
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    for step in range(200):
        sess.run(train)
        if step%20==0:
            print(step,'[k,b]:',sess.run([k,b]))