kl_loss.py

import numpy as np


def kl_loss(y_true, y_pred):
    return np.sum(y_true * np.log(y_true / y_pred))


y_true = np.array([0.1, 0.5, 0.2, 0.15, 0.05])
y_pr_1 = np.array([0.15, 0.4, 0.2, 0.2, 0.05])
y_pr_2 = np.array([0.5, 0.1, 0.1, 0.2, 0.1])

print(np.sum(y_true), np.sum(y_pr_2), np.sum(y_pr_1))
print(kl_loss(y_true, y_pr_1))  # => 0.02 because y_pr_1 is similar to y_true
print(kl_loss(y_true, y_pr_2))  # => 0.70