soft margin smooth hinge

triplet_loss.py

def triplet_loss_soft(anchor, positive, negative, m=1):
    """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'):
        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), m * tf.nn.softplus(pos_dist))

        diff = tf.subtract(pos_dist, neg_dist)
        margin = m * tf.nn.softplus(pos_dist)
        loss = tf.relu(tf.add(diff, margin))
        quad = tf.minimum(loss, m)
        linear = m * ( loss - quad)
        total_loss = tf.square(quad) + 2 * margin * linear
        total_loss = tf.reduce_mean(total_loss)
    return total_loss