Warp Loss in tensorflow -- http://www.thespermwhale.com/jaseweston/papers/wsabie-ijcai.pdf

tf_warp.py

def rankL(np_rank):
    r = int(np_rank[-1])
    _l = 0
    for k in range(1, r+1):
        _l += 1./k
    return np.float32(_l)


"""
labels are assumed to be 1 hot encoded
"""
def warp_loss(labels, logits):
    # for easy broadcasting
    labels, logits = tf.transpose(labels, [1, 0]), tf.transpose(logits, [1, 0])
    f_y = tf.reduce_sum(logits*labels, axis=0)
    rank = tf.reduce_sum(tf.maximum(tf.sign(1+logits-f_y), 0), axis=0)
    diff = tf.reduce_sum(tf.maximum(1+logits-f_y, 0), axis=0)
    with tf.control_dependencies([tf.assert_greater(rank, tf.zeros_like(rank))]):
        return tf.py_func(rankL, [rank], tf.float32) * diff/rank