text: import tensorflow as tf
  
  # data
  x_data = [[0., 2., 0., 4., 0.],
            [1., 0., 3., 0., 5.]]
  y_data = [1, 2, 3, 4, 5]
  
  # Try to find values for W
  W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))
  b = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
  
  # Our hypothesis
  hypothesis = tf.matmul(W, x_data) + b
  
  # Simplified cost function
  cost = tf.reduce_mean(tf.square(hypothesis - y_data))
  
  # Minimize
  a = tf.Variable(0.1)  # Learning rate, alpha
  optimizer = tf.train.GradientDescentOptimizer(a)
  train = optimizer.minimize(cost)
  
  # Before starting, initialize the variables. We will 'run' this first.
  init = tf.initialize_all_variables()
  
  # Launch the graph
  sess = tf.Session()
  sess.run(init)
  
  # Fit the line.
  for step in xrange(2001):
      sess.run(train)
      if step % 20 == 0:
          print step, sess.run(cost), sess.run(W), sess.run(b)
  