tensorflow张量排序

本篇记录一下TensorFlow中张量的排序方法

tf.sort和tf.argsort

# 声明tensor a是由1到5打乱顺序组成的
a = tf.random.shuffle(tf.range(5))
# 打印排序后的tensor
print(tf.sort(a,direction='DESCENDING').numpy())
# 打印从大到小排序后，数字对应原来的索引
print(tf.argsort(a,direction='DESCENDING').numpy())
index = tf.argsort(a,direction='DESCENDING')
# 按照索引序列取值
print(tf.gather(a,index))

# 返回最大的两个值信息
res = tf.math.top_k(a,2)
# indices返回索引
print(res.indices)
# values返回值
print(res.values)

计算准确率实例：

# 定义模型输出预测概率
prob = tf.constant([[0.1,0.2,0.7],[0.2,0.7,0.1]])
# 定义y标签
target = tf.constant([2,0])
# 求top3的索引
k_b = tf.math.top_k(prob,3).indices
# 将矩阵进行转置，即把top-1,top-2,top-3分组
print(tf.transpose(k_b,[1,0]))
# 将y标签扩展成与top矩阵相同维度的tensor，方便比较
target = tf.broadcast_to(target,[3,2])

# 实现求准确率的方法
def accuracy(output,target,topk=(1,)):
    maxk = max(topk)
    batch_size = target.shape[0]

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

    res = []
    for k in topk:
        correct_k = tf.cast(tf.reshape(correct[:k],[-1]),dtype=tf.float32)
        correct_k = tf.reduce_sum(correct_k)
        acc = float(correct_k/batch_size)
        res.append(acc)
    return res

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)
    batch_size = target.shape[0]

    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)
        correct_k = tf.reduce_sum(correct_k)
        acc = float(correct_k* (100.0 / batch_size) )
        res.append(acc)

    return res



output = tf.random.normal([10, 6])
output = tf.math.softmax(output, axis=1)
target = tf.random.uniform([10], maxval=6, dtype=tf.int32)
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))
print('top-1-6 acc:', acc)

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