old_contents: """
  To know more or get code samples, please visit my website:
  https://morvanzhou.github.io/tutorials/
  Or search: 莫烦Python
  Thank you for supporting!
  """
  
  # please note, all tutorial code are running under python3.5.
  # If you use the version like python2.7, please modify the code accordingly
  
  # 4 - Regressor example
  
  import numpy as np
  np.random.seed(1337)  # for reproducibility
  from keras.models import Sequential
  from keras.layers import Dense
  import numpy as np
  import matplotlib.pyplot as plt
  
  # create some data
  X = np.linspace(-1, 1, 200)
  np.random.shuffle(X)    # randomize the data
  Y = 0.5 * X + 2 + np.random.normal(0, 0.05, (200, ))
  # plot data
  plt.scatter(X, Y)
  plt.show()
  
  X_train, Y_train = X[:160], Y[:160]     # first 160 data points
  X_test, Y_test = X[160:], Y[160:]       # last 40 data points
  
  # build a neural network from the 1st layer to the last layer
  model = Sequential()
  model.add(Dense(output_dim=1, input_dim=1))
  
  # choose loss function and optimizing method
  model.compile(loss='mse', optimizer='sgd')
  
  # training
  print('Training -----------')
  for step in range(301):
      cost = model.train_on_batch(X_train, Y_train)
      if step % 100 == 0:
          print('train cost: ', cost)
  
  # test
  print('\nTesting ------------')
  cost = model.evaluate(X_test, Y_test, batch_size=40)
  print('test cost:', cost)
  W, b = model.layers[0].get_weights()
  print('Weights=', W, '\nbiases=', b)
  
new_contents: """
  To know more or get code samples, please visit my website:
  https://morvanzhou.github.io/tutorials/
  Or search: 莫烦Python
  Thank you for supporting!
  """
  
  # please note, all tutorial code are running under python3.5.
  # If you use the version like python2.7, please modify the code accordingly
  
  # 4 - Regressor example
  
  import numpy as np
  np.random.seed(1337)  # for reproducibility
  from keras.models import Sequential
  from keras.layers import Dense
  import matplotlib.pyplot as plt
  
  # create some data
  X = np.linspace(-1, 1, 200)
  np.random.shuffle(X)    # randomize the data
  Y = 0.5 * X + 2 + np.random.normal(0, 0.05, (200, ))
  # plot data
  plt.scatter(X, Y)
  plt.show()
  
  X_train, Y_train = X[:160], Y[:160]     # first 160 data points
  X_test, Y_test = X[160:], Y[160:]       # last 40 data points
  
  # build a neural network from the 1st layer to the last layer
  model = Sequential()
  model.add(Dense(output_dim=1, input_dim=1))
  
  # choose loss function and optimizing method
  model.compile(loss='mse', optimizer='sgd')
  
  # training
  print('Training -----------')
  for step in range(301):
      cost = model.train_on_batch(X_train, Y_train)
      if step % 100 == 0:
          print('train cost: ', cost)
  
  # test
  print('\nTesting ------------')
  cost = model.evaluate(X_test, Y_test, batch_size=40)
  print('test cost:', cost)
  W, b = model.layers[0].get_weights()
  print('Weights=', W, '\nbiases=', b)
  
  # plotting the prediction
  Y_pred = model.predict(X_test)
  plt.scatter(X_test, Y_test)
  plt.plot(X_test, Y_pred)
  plt.show()
subject: edit regressor