import numpy as np
 
# Write a function that takes as input a list of numbers, and returns
# the list of values given by the softmax function.
def softmax(L):
 pass
 expL = np.exp(L)
 sumExpL = sum(expL)
 result = []
 for i in expL:
  result.append(i*1.0/sumExpL)
 return result

import numpy as np
 
def cross_entropy(Y, P):
 Y = np.float_(Y)
 P = np.float_(P)
 return -np.sum(Y * np.log(P) + (1 - Y) * np.log(1 - P))