text: import tensorflow as tf
  import numpy as np
  
  x_data = np.random.rand(100)
  y_data = x_data*0.1 + 0.2
  
  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))
  # 定义一个梯度下降法来进行训练优化器
  optimizer = tf.train.GradientDescentOptimizer(0.2)
  # 最小化loss
  train = optimizer.minimize(loss)
  # 初始化变量
  init = tf.global_variables_initializer()
  
  with tf.Session() as sess:
      sess.run(init)
      for step in range(10000):
          sess.run(train)
          if step % 20 == 0:
              print(step, sess.run([k, b]))
  