np.random.rand VS np.random.randn

• np.random.rand()
  Create an array of the given shape and populate it with random samples from a uniform distribution (均匀分布) over [0, 1).
  
  Example:
  >>> np.random.rand(3,2)
  　　array([[ 0.14022471, 0.96360618], 　#random
  　　[ 0.37601032, 0.25528411], 　　　　 #random
  　　[ 0.49313049, 0.94909878]])　　　　 #random
  
  
• np.random.randn()
  Return a sample (or samples) from the “standard normal” distribution(标准正态分布).
  
  Notes
  For random samples from N(mu, sigma^2), use:
  >>> sigma * np.random.randn(…) + mu
  
  Example:
  >>> np.random.randn()
  2.1923875335537315 #random
  
  Two-by-four array of samples from N(3, 6.25):
  
  >>> 2.5 * np.random.randn(2, 4) + 3
  array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random
  [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random