differentiating cross entropy

cross_entropy_prime.py

import torch 

zs = torch.tensor([[0.1, 0.4, 0.2], [0.3, 0.9, 0.6]]) # The values of 3 output neurons for 2 instances
activations = softmax(zs) # = [[0.2894, 0.3907, 0.3199],[0.2397, 0.4368, 0.3236]]
y = torch.tensor([2,0]) # equivalent to [[0,0,1],[1,0,0]]

#----------- Implementing the math -----------#
def crossentropy_prime(activations, labels):
  n = labels.shape[0]
  activs = torch.zeros_like(activations)
  activs[range(n), labels] = -1 / activations[range(n), labels] # integer array indexing
  return activs 

c_p = crossentropy_prime(activations, y)

#----------- Printing Output -----------#
print(f"Cross-entropy differentiation: \n{c_p}\n")
'''
Out:
Cross-entropy differentiation: 
tensor([[ 0.0000,  0.0000, -3.1262],
        [-4.1720,  0.0000,  0.0000]])
'''