code: # -*- coding: utf-8 -*-
  # Author: XuMing <shibing624@126.com>
  # Data: 17/10/10
  # Brief: 
  import tensorflow as tf
  import numpy as np
  
  # 使用 NumPy 生成假数据(phony data), 总共 100 个点.
  x_data = np.float32(np.random.rand(2, 100)) # 随机输入
  y_data = np.dot([0.100, 0.200], x_data) + 0.300
  
  # 构造一个线性模型
  #
  b = tf.Variable(tf.zeros([1]))
  W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))
  y = tf.matmul(W, x_data) + b
  
  # 最小化方差
  loss = tf.reduce_mean(tf.square(y - y_data))
  optimizer = tf.train.GradientDescentOptimizer(0.5)
  train = optimizer.minimize(loss)
  
  # 初始化变量
  init = tf.global_variables_initializer()
  
  # 启动图 (graph)
  sess = tf.Session()
  sess.run(init)
  
  # 拟合平面
  for step in range(0, 201):
      sess.run(train)
      if step % 20 == 0:
          print(step, sess.run(W), sess.run(b))
  
  # 得到最佳拟合结果 W: [[0.100  0.200]], b: [0.300]
apis: tensorflow.zeros tensorflow.global_variables_initializer tensorflow.matmul tensorflow.random_uniform numpy.random.rand tensorflow.train.GradientDescentOptimizer tensorflow.Session tensorflow.square numpy.dot