def cross_entropy_error(Y, probas_):
    # compute Cross Entropy using the Natural Log:
    # ( -tln(y) ) − ( (1−t)ln(1−y) )
    probas_class1 = probas_[:, 1]  # get the class 1 probabilities
    cross_entropy_errors = ((-Y) * (np.log(probas_class1))) - ((1 - Y) * (np.log(probas_class1 - 1)))
    return cross_entropy_errors
dataset/ETHPy150Open ianozsvald/social_media_brand_disambiguator/learn1_experiments_tfidfproper.py/cross_entropy_error/swapped operands of `-`