text: import tensorflow as tf
  import numpy as np
  
  # Make 100 phony data points in NumPy.
  x_data = np.float32(np.random.rand(2, 100)) # Random input
  y_data = np.dot([0.100, 0.200], x_data) + 0.300
  
  # Construct a linear model.
  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
  
  # Minimize the squared errors.
  loss = tf.reduce_mean(tf.square(y - y_data))
  optimizer = tf.train.GradientDescentOptimizer(0.5)
  train = optimizer.minimize(loss)
  
  # For initializing the variables.
  init = tf.initialize_all_variables()
  
  # Launch the graph
  sess = tf.Session()
  sess.run(init)
  
  # Fit the plane.
  for step in xrange(0, 201):
      sess.run(train)
      if step % 20 == 0:
      	print step, sess.run(W), sess.run(b)
  
  		# Learns best fit is W: [[0.100  0.200]], b: [0.300]
  