1、其中主要的损失triplet loss 原理

triplet loss代码如下：

def triplet_loss(anchor, positive, negative, alpha):
    """Calculate the triplet loss according to the FaceNet paper
    Args:
      anchor: the embeddings for the anchor images.
      positive: the embeddings for the positive images.
      negative: the embeddings for the negative images.
    Returns:
      the triplet loss according to the FaceNet paper as a float tensor.
    """
    with tf.variable_scope('triplet_loss'):
        #进行网络的subtract减法、tf.square平方、reduce_sum
        pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), 1)
        neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), 1)
 
        basic_loss = tf.add(tf.subtract(pos_dist,neg_dist), alpha)
        loss = tf.reduce_mean(tf.maximum(basic_loss, 0.0), 0)
 
    return loss

2、模型的准确度评估

• 评价模型准确率时，利用lfw的pairs做varification。accuracy很好理解，具体参考facenet.calculate_roc()，利用10折交叉验证，找出最佳accuracy的阈值，从0-4步长0.01.一般在1.2左右。
• 而Validation rate，和far这个判断值搞了半天才搞清楚，这个值是在假定误把不同的人判断为相同的人的概率设定为FAIR=0.001的情况下，判断两个人是同一个人判断正确的概率。这个是为了尽可能降低将不同人判断为同一个人的概率。

def calculate_val_far(threshold, dist, actual_issame):
    predict_issame = np.less(dist, threshold)
    true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
    false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
    n_same = np.sum(actual_issame)
    n_diff = np.sum(np.logical_not(actual_issame))
    val = float(true_accept) / float(n_same)
    far = float(false_accept) / float(n_diff)
    return val, far