def center_loss(features, label, alfa, nrof_classes):

    """Center loss based on the paper "A Discriminative Feature Learning Approach for Deep Face Recognition"

       (http://ydwen.github.io/papers/WenECCV16.pdf)

       https://blog.csdn.net/u014380165/article/details/76946339

    """

    nrof_features = features.get_shape()[]
　　#训练过程中，需要保存当前所有类中心的全连接预测特征centers， 每个batch的计算都要先读取已经保存的centers

    centers = tf.get_variable('centers', [nrof_classes, nrof_features], dtype=tf.float32,

        initializer=tf.constant_initializer(), trainable=False)

    label = tf.reshape(label, [-])

    centers_batch = tf.gather(centers, label)#获取当前batch对应的类中心特征

    diff = ( - alfa) * (centers_batch - features)#计算当前的类中心与特征的差异，用于Cj的的梯度更新，这里facenet的作者做了一个 1-alfa操作，比较奇怪，和原论文不同

    centers = tf.scatter_sub(centers, label, diff)#更新梯度Cj，对于上图中步骤6，tensorflow会将该变量centers保留下来，用于计算下一个batch的centerloss

    loss = tf.reduce_mean(tf.square(features - centers_batch))#计算当前的centerloss 对应于Lc

    return loss, centers