一、tf.sort()排序，tf.argsort()排序得到元素index

 

 

 

 二、top-k之tf.math.top_k()最大的前k个元素

 

 

 

 

 

 三、实战

import tensorflow as tf
import os

os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2'
tf.random.set_seed(2467)

def accuracy(output, target, topk=(1,)):
    maxk = max(topk)                        #这里为6
    batch_size = target.shape[0]            #tensor的行数

    pred = tf.math.top_k(output, maxk).indices
    pred = tf.transpose(pred, perm=[1, 0])
    target_ = tf.broadcast_to(target, pred.shape)
    # [10, b]
    correct = tf.equal(pred, target_)

    res = []
    for k in topk:
        correct_k = tf.cast(tf.reshape(correct[:k], [-1]), dtype=tf.float32)    #到第几行，参考上一个图片！
        correct_k = tf.reduce_sum(correct_k)
        acc = float(correct_k* (100.0 / batch_size) )
        res.append(acc)

    return res

#用正太分布生成10个样本，6类，然后进行一个softmax.
output = tf.random.normal([10, 6])
output = tf.math.softmax(output, axis=1)
target = tf.random.uniform([10], maxval=6, dtype=tf.int32) #0-5之间随机10个数。
print('prob:', output.numpy())
pred = tf.argmax(output, axis=1)
print('pred:', pred.numpy())
print('label:', target.numpy())

acc = accuracy(output, target, topk=(1,2,3,4,5,6))  #top1~top6的accuracy
print('top-1-6 acc:', acc)