text: # Cosine annealing learning rate schedule
  # https://machinelearningmastery.com/snapshot-ensemble-deep-learning-neural-network/
  
  import superimport
  
  #from matplotlib import pyplot
  from math import pi
  from math import cos
  from math import floor
   
  import numpy as np
  import matplotlib.pyplot as plt
  import os
  
  
  figdir = "../figures"
  def save_fig(fname):
      if figdir: plt.savefig(os.path.join(figdir, fname))
      
  # cosine annealing learning rate schedule
  def cosine_annealing(epoch, n_epochs, n_cycles, lrate_max):
  	epochs_per_cycle = floor(n_epochs/n_cycles)
  	cos_inner = (pi * (epoch % epochs_per_cycle)) / (epochs_per_cycle)
  	return lrate_max/2 * (cos(cos_inner) + 1)
   
  # create learning rate series
  n_epochs = 100
  n_cycles = 5
  lrate_max = 0.01
  series = [cosine_annealing(i, n_epochs, n_cycles, lrate_max) for i in range(n_epochs)]
  # plot series
  plt.figure()
  plt.plot(series)
  fname = 'lrschedule_cosine_annealing.pdf'
  save_fig(fname)
  plt.show()
  
  