code: import numpy as np
  import tensorflow as tf
  import matplotlib.pyplot as plt
  from logistic_adam import *
  
  
  #learing rate = 1,batch_size = 500, epoch=15, lamda = 0.01
  logging = runLogistic(1,500,15,0.01)
  #print(logging)
  plt.plot(logging[:,0],marker='+',label='learning rate = 1')
  
  #learing rate = 0.1,batch_size = 500, epoch=15, lamda = 0.01
  logging = runLogistic(0.1,500,15,0.01)
  #print(logging)
  plt.plot(logging[:,0],marker='*',label='learning rate = 0.1')
  
  #learing rate = 0.01,batch_size = 500, epoch=15, lamda = 0.01
  logging = runLogistic(0.01,500,15,0.01)
  #print(logging)
  plt.plot(logging[:,0],marker='h',label='learning rate = 0.01')
  
  #learing rate = 0.001,batch_size = 500, epoch=15, lamda = 0.01
  logging = runLogistic(0.001,500,15,0.01)
  #print(logging)
  plt.plot(logging[:,0],marker='d',label='learning rate = 0.001')
  
  
  plt.legend(loc='upper right')
  plt.title('Plot of Train_CrossEntropy vs. Iterations with batch_size=500')
  plt.xlabel('Iterations')
  plt.ylabel('Train_CrossEntropy')
  plt.show()
  
  
  
apis: matplotlib.pyplot.legend matplotlib.pyplot.title matplotlib.pyplot.show matplotlib.pyplot.ylabel matplotlib.pyplot.plot matplotlib.pyplot.xlabel