Tried to write class-balanced cross entropy, but couldn't understand it. about east HOT 5 CLOSED

def weighted_cross_entropy(y_true_cls, y_pred_cls,
                           training_mask):
    # Loss for score Map
    epsilon = 10e-8
    # assume ratio of positive class is 0.1
    beta = 1.0 - 0.1
    # turn probabilities back to logits
    y_pred_cls = tf.clip_by_value(y_pred_cls, epsilon, 1 - epsilon)
    y_pred_cls = tf.log(y_pred_cls / (1. - y_pred_cls))
    Ls = (1. - beta) * tf.nn.weighted_cross_entropy_with_logits(targets=y_true_cls, logits=y_pred_cls,
                                                                pos_weight=beta / (1. - beta))
    Ls = tf.reduce_mean(Ls * training_mask)
    tf.summary.scalar('classification_logloss', Ls)
    return Ls