old_contents: # View more python learning tutorial on my Youtube and Youku channel!!!
  
  # Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg
  # Youku video tutorial: http://i.youku.com/pythontutorial
  
  import tensorflow as tf
  import numpy as np
  
  def add_layer(inputs, in_size, out_size, activation_function=None):
      Weights = tf.Variable(tf.random_normal([in_size, out_size]))
      biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
      Wx_plus_b = tf.matmul(inputs, Weights) + biases
      if activation_function is None:
          outputs = Wx_plus_b
      else:
          outputs = activation_function(Wx_plus_b)
      return outputs
  
  # Make up some real data
  x_data = np.linspace(-1,1,300)[:, np.newaxis]
  noise = np.random.normal(0, 0.05, x_data.shape)
  y_data = np.square(x_data) - 0.5 + noise
  
  # define placeholder for inputs to network
  xs = tf.placeholder(tf.float32, [None, 1])
  ys = tf.placeholder(tf.float32, [None, 1])
  # add hidden layer
  l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
  # add output layer
  prediction = add_layer(l1, 10, 1, activation_function=None)
  
  # the error between prediciton and real data
  loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
                       reduction_indices=[1]))
  train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
  
  # important step
  init = tf.initialize_all_variables()
  sess = tf.Session()
  sess.run(init)
  
  for i in range(1000):
      # training
      sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
      if i % 50 == 0:
          # to see the step improvement
          print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))
new_contents: # View more python learning tutorial on my Youtube and Youku channel!!!
  
  # Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg
  # Youku video tutorial: http://i.youku.com/pythontutorial
  
  import tensorflow as tf
  import numpy as np
  import matplotlib.pyplot as plt
  
  def add_layer(inputs, in_size, out_size, activation_function=None):
      Weights = tf.Variable(tf.random_normal([in_size, out_size]))
      biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
      Wx_plus_b = tf.matmul(inputs, Weights) + biases
      if activation_function is None:
          outputs = Wx_plus_b
      else:
          outputs = activation_function(Wx_plus_b)
      return outputs
  
  # Make up some real data
  x_data = np.linspace(-1, 1, 300)[:, np.newaxis]
  noise = np.random.normal(0, 0.05, x_data.shape)
  y_data = np.square(x_data) - 0.5 + noise
  
  ##plt.scatter(x_data, y_data)
  ##plt.show()
  
  # define placeholder for inputs to network
  xs = tf.placeholder(tf.float32, [None, 1])
  ys = tf.placeholder(tf.float32, [None, 1])
  # add hidden layer
  l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
  # add output layer
  prediction = add_layer(l1, 10, 1, activation_function=None)
  
  # the error between prediciton and real data
  loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction), reduction_indices=[1]))
  train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
  # important step
  init = tf.initialize_all_variables()
  sess= tf.Session()
  sess.run(init)
  
  for i in range(1000):
      # training
      sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
      if i % 50 == 0:
          # to see the step improvement
          print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))
  
subject: create tf 11