tensorflow张量排序

# 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)

# 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 osos.environ['TF_CPP_MIN_LOG_LEVEL'] = ''
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 resoutput = 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)