new_contents: import tensorflow as tf
  import numpy as np
  
  x_data = np.random.rand(100).astype("float32")
  y_data = x_data * .1 +.3
  W = tf.Variable(tf.random_uniform([1], -1.0, 1.0 ))
  b = tf.Variable(tf.zeros([1]))
  y = W * x_data + b
  
  loss = tf.reduce_mean(tf.square(y - y_data ))
  optimizer = tf.train.GradientDescentOptimizer(0.5)
  train = optimizer.minimize(loss)
  
  init = tf.initialize_all_variables()
  
  sess = tf.Session()
  sess.run(init)
  
  for step in xrange(201):
    sess.run(train)
    if step % 20 == 0:
      print (step, sess.run(W), sess.run(b))
  
subject: Create y=Wx+b.py