old_contents: 
  # coding: utf-8
  
  # In[2]:
  
  """
      tf_xy_fit.py - Yevheniy Chuba - 11/11/2016
      Create random data in 2D and fit a line to it using TensorFlow
      
      Following turorial @:
          https://www.tensorflow.org/versions/r0.11/get_started/index.html
  """
  
  import tensorflow as tf
  import numpy as np
  
  # Create 100 random x, y data points in NumPy, y = x * 0.1 + 0.3
  x_data = np.random.rand(100).astype(np.float32)
  y_data = x_data * 0.1 + 0.3
  
  # Try to find values for W and b that compute y_data = W + x_data + b
  # W and b are known, but we will TensorFlow figure it out
  W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
  b = tf.Variable(tf.zeros([1]))
  y = W * x_data + b
  
  # Minimize the mean squared errors
  loss = tf.reduce_mean(tf.square(y - y_data))
  optimizer = tf.train.GradientDescentOptimizer(0.5)
  train = optimizer.minimize(loss)
  
  # 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 range(201):
      sess.run(train)
      if step % 20 == 0:
          print(step, sess.run(W), sess.run(b))
  
  
  # In[11]:
  
  # create two matrixes and multiply them
  matrix1 = tf.constant([[3., 3.]])   # 1X2 matrix
  matrix2 = tf.constant([[2.], [2.]]) # 2x1 matrix
  # Create a Matmul (matrix multiplier) op that takes both matrixes as inputs
  product = tf.matmul(matrix1, matrix2)
  print ("product of both matrixes: ", product)
  
  
  # In[ ]:
  
  
  
  
new_contents: 
  # coding: utf-8
  
  # In[2]:
  
  """
      tf_xy_fit.py - Yevheniy Chuba - 11/11/2016
      Create random data in 2D and fit a line to it using TensorFlow
      
      Following turorial @:
          https://www.tensorflow.org/versions/r0.11/get_started/index.html
  """
  
  import tensorflow as tf
  import numpy as np
  
  # Create 100 random x, y data points in NumPy, y = x * 0.1 + 0.3
  x_data = np.random.rand(100).astype(np.float32)
  y_data = x_data * 0.1 + 0.3
  
  # Try to find values for W and b that compute y_data = W + x_data + b
  # W and b are known, but we will TensorFlow figure it out
  W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
  b = tf.Variable(tf.zeros([1]))
  y = W * x_data + b
  
  # Minimize the mean squared errors
  loss = tf.reduce_mean(tf.square(y - y_data))
  optimizer = tf.train.GradientDescentOptimizer(0.5)
  train = optimizer.minimize(loss)
  
  # 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 range(201):
      sess.run(train)
      if step % 20 == 0:
          print(step, sess.run(W), sess.run(b))
  
  
  # create two matrixes and multiply them
  matrix1 = tf.constant([[3., 3.]])   # 1X2 matrix
  matrix2 = tf.constant([[2.], [2.]]) # 2x1 matrix
  # Create a Matmul (matrix multiplier) op that takes both matrixes as inputs
  product = tf.matmul(matrix1, matrix2)
  print ("product of both matrixes: ", product)
  
  
  # In[ ]:
  
  
  
  
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