def weighted_log_loss(yt, yp) -> Any:
    """
    Binary crossentropy with a bias towards false negatives
    yt: Target
    yp: Prediction
    """
    from keras import backend as K

    pos_loss = -(0 + yt) * K.log(0 + yp + K.epsilon())
    neg_loss = -(1 - yt) * K.log(1 - yp + K.epsilon())

    return LOSS_BIAS * K.mean(neg_loss) + (1. - LOSS_BIAS) * K.mean(pos_loss)