TensorFlow2-高阶操作（四）：张量排序【sort（返回排序后的新张量）、argsort（返回排序后的索引组成的张量）、Topk（返回排列后的前k个元素组成的张量）、Top-k准确度】
2023-09-27 14:20:41 时间
一、tf.sort：返回排序后的新Tensor
tf.sort：返回排序后的新Tensor
也可以对多维Tensor进行排序，当对多维Tensor进行排序时，可以通过axis参数指定需要排序的维度，axis的默认值为-1，也就是对最后一维进行排序。
1、向量排序
import tensorflow as tf
a = tf.random.uniform([5], maxval=10, dtype=tf.int32)
print("a = ", a)
print("-" * 100)
b = tf.sort(a, direction='DESCENDING')
print("a = ", a)
print("-" * 50)
print("b = ", b)
print("-" * 100)
打印结果：
a = tf.Tensor([4 9 5 7 2], shape=(5,), dtype=int32)
----------------------------------------------------------------------------------------------------
a = tf.Tensor([4 9 5 7 2], shape=(5,), dtype=int32)
--------------------------------------------------
b = tf.Tensor([9 7 5 4 2], shape=(5,), dtype=int32)
----------------------------------------------------------------------------------------------------
Process finished with exit code 0
2、矩阵排序
import tensorflow as tf
a = tf.random.uniform([3, 3], maxval=10, dtype=tf.int32)
print("a = ", a)
print("-" * 100)
b = tf.sort(a, direction='DESCENDING')
print("a = ", a)
print("-" * 50)
print("b = ", b)
print("-" * 100)
打印结果：
a = tf.Tensor(
[[3 8 8]
[6 4 7]
[2 7 5]], shape=(3, 3), dtype=int32)
----------------------------------------------------------------------------------------------------
a = tf.Tensor(
[[3 8 8]
[6 4 7]
[2 7 5]], shape=(3, 3), dtype=int32)
--------------------------------------------------
b = tf.Tensor(
[[8 8 3]
[7 6 4]
[7 5 2]], shape=(3, 3), dtype=int32)
----------------------------------------------------------------------------------------------------
Process finished with exit code 0
二、tf.argsort：返回排序之后的索引组成的Tensor
1、向量排序
import tensorflow as tf
a = a = tf.constant([2, 0, 3, 4, 1])
print("a = ", a)
print("-" * 100)
b = tf.argsort(a, direction='DESCENDING')
print("a = ", a)
print("-" * 50)
print("b = ", b)
print("-" * 100)
打印结果：
a = tf.Tensor([2 0 3 4 1], shape=(5,), dtype=int32)
----------------------------------------------------------------------------------------------------
a = tf.Tensor([2 0 3 4 1], shape=(5,), dtype=int32)
--------------------------------------------------
b = tf.Tensor([3 2 0 4 1], shape=(5,), dtype=int32)
----------------------------------------------------------------------------------------------------
Process finished with exit code 0
2、矩阵排序
import tensorflow as tf
a = tf.random.uniform([3, 3], maxval=10, dtype=tf.int32)
print("a = ", a)
print("-" * 100)
b = tf.argsort(a, direction='DESCENDING')
print("a = ", a)
print("-" * 50)
print("b = ", b)
print("-" * 100)
打印结果：
a = tf.Tensor(
[[5 0 2]
[1 9 5]
[5 6 7]], shape=(3, 3), dtype=int32)
----------------------------------------------------------------------------------------------------
a = tf.Tensor(
[[5 0 2]
[1 9 5]
[5 6 7]], shape=(3, 3), dtype=int32)
--------------------------------------------------
b = tf.Tensor(
[[0 2 1]
[1 2 0]
[2 1 0]], shape=(3, 3), dtype=int32)
----------------------------------------------------------------------------------------------------
Process finished with exit code 0
三、Topk（tf.math.top_k()）:只返回最值的前几个元素值和索引号
只返回最值的前几个元素值和索引号
tf.math.top_k(
input,
k=1,
sorted=True, //默认降序
name=None
)
1、向量排序
import tensorflow as tf
a = tf.random.uniform([5], maxval=10, dtype=tf.int32)
print("a = ", a)
print("-" * 100)
b = tf.math.top_k(input=a, k=3)
print("a = ", a)
print("-" * 50)
print("b = ", b)
print("-" * 50)
print("b.values = ", b.values)
print("-" * 50)
print("b.indices = ", b.indices)
print("-" * 100)
打印结果：
a = tf.Tensor([7 3 2 2 9], shape=(5,), dtype=int32)
----------------------------------------------------------------------------------------------------
a = tf.Tensor([7 3 2 2 9], shape=(5,), dtype=int32)
--------------------------------------------------
b = TopKV2(values=<tf.Tensor: shape=(3,), dtype=int32, numpy=array([9, 7, 3])>, indices=<tf.Tensor: shape=(3,), dtype=int32, numpy=array([4, 0, 1])>)
--------------------------------------------------
b.values = tf.Tensor([9 7 3], shape=(3,), dtype=int32)
--------------------------------------------------
b.indices = tf.Tensor([4 0 1], shape=(3,), dtype=int32)
----------------------------------------------------------------------------------------------------
Process finished with exit code 0
2、矩阵排序
返回a矩阵中，每一行最大的k个值及其索引。
import tensorflow as tf
a = tf.random.uniform([5, 5], maxval=10, dtype=tf.int32)
print("a = ", a)
print("-" * 100)
b = tf.math.top_k(input=a, k=3)
print("a = ", a)
print("-" * 50)
print("b = ", b)
print("-" * 50)
print("b.values = ", b.values)
print("-" * 50)
print("b.indices = ", b.indices)
print("-" * 100)
打印结果：
a = tf.Tensor(
[[4 3 7 0 2]
[2 0 6 8 5]
[3 8 4 2 9]
[1 5 9 0 6]
[8 4 2 2 1]], shape=(5, 5), dtype=int32)
----------------------------------------------------------------------------------------------------
a = tf.Tensor(
[[4 3 7 0 2]
[2 0 6 8 5]
[3 8 4 2 9]
[1 5 9 0 6]
[8 4 2 2 1]], shape=(5, 5), dtype=int32)
--------------------------------------------------
b = TopKV2(values=<tf.Tensor: shape=(5, 3), dtype=int32, numpy=
array([[7, 4, 3],
[8, 6, 5],
[9, 8, 4],
[9, 6, 5],
[8, 4, 2]])>, indices=<tf.Tensor: shape=(5, 3), dtype=int32, numpy=
array([[2, 0, 1],
[3, 2, 4],
[4, 1, 2],
[2, 4, 1],
[0, 1, 2]])>)
--------------------------------------------------
b.values = tf.Tensor(
[[7 4 3]
[8 6 5]
[9 8 4]
[9 6 5]
[8 4 2]], shape=(5, 3), dtype=int32)
--------------------------------------------------
b.indices = tf.Tensor(
[[2 0 1]
[3 2 4]
[4 1 2]
[2 4 1]
[0 1 2]], shape=(5, 3), dtype=int32)
----------------------------------------------------------------------------------------------------
Process finished with exit code 0
四、Top-k 准确度
应用：准确度
- 假设pred预测概率为[0.1, 0.2, 0.3, 0.4]，即最有可能预测值为3
- 假设正确值为2
- 利用tf.math.top_k(pred,1) 返回3 => 准确度为0%
- 利用tf.math.top_k(pred,2) 返回[3, 2] => 假设前两个中有正确值即可，准确度为100%
- ImageNet 考虑Top-5，假设1000个种类中前五个预测中含有正确值，准确率认为100%
1、Top-5 准确度
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
tf.random.set_seed(2467)
# output为神经网络输出值
# target为实际值
# topk为想输出的top_k的值
def accuracy(output, target, topk):
print("计算准确度：")
print("\t", "-" * 50)
batch_size = target.shape[0]
print("\tbatch_size = ", batch_size)
print("\t", "-" * 50)
pred_top_k = tf.math.top_k(input=output, k=topk).indices # 计算top_k的索引，也即预测值
print("\tpred_top_k = ", pred_top_k)
print("\t", "-" * 50)
pred_top_k = tf.transpose(pred_top_k, perm=[1, 0]) # 将索引值进行转置
print("\t转置之后：pred_top_k = ", pred_top_k)
print("\t", "-" * 50)
target_ = tf.broadcast_to(target, pred_top_k.shape) # 对target进行扩充
print("\ttarget_ = ", target_)
print("\t", "-" * 50)
equal_result = tf.equal(pred_top_k, target_) # 比较输出结果
print("\tequal_result = ", equal_result)
print("\t", "-" * 50)
res = []
equal_count_sum = tf.reduce_sum(tf.cast(equal_result, dtype=tf.float32))
print("\tequal_count_sum = ", equal_count_sum)
acc = float(equal_count_sum * (100.0 / batch_size))
res.append(acc)
return res
output = tf.random.normal([10, 6]) # 生成一个六类的样本，样本数为10
output = tf.math.softmax(output, axis=1) # softmax可以使得六类的概率为1
print('output = \n', output.numpy())
print("-" * 200)
pred = tf.argmax(output, axis=1)
print('\nprob = ', pred.numpy())
# 假设分类目标标签为：
target = tf.constant([0, 2, 3, 4, 2, 4, 2, 3, 5, 5]) # 结果为【0~5】的10个样本
print('target = ', target.numpy())
print("-" * 200)
acc = accuracy(output, target, topk=5) # 调用精确度计算函数
print("-" * 200)
print('\ntop-5 acc:', acc)
打印结果：
output =
[[0.25310278 0.21715644 0.16043882 0.13088997 0.04334084 0.19507109]
[0.05892418 0.04548917 0.00926314 0.14529602 0.66777605 0.07325139]
[0.09742809 0.08304427 0.07460099 0.04067177 0.626185 0.07806987]
[0.20478569 0.12294925 0.12010485 0.13751233 0.36418733 0.05046057]
[0.11872064 0.31072393 0.12530337 0.15528883 0.21325873 0.07670453]
[0.01519807 0.09672114 0.1460476 0.00934331 0.5649092 0.1677807 ]
[0.04199061 0.18141054 0.06647632 0.6006175 0.03198383 0.07752118]
[0.09226219 0.23460893 0.13022321 0.16295876 0.05362028 0.32632664]
[0.07019574 0.08611772 0.10912607 0.10521299 0.2152082 0.4141393 ]
[0.01882887 0.2659769 0.19122466 0.2410926 0.14920163 0.1336753 ]]
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
prob = [0 4 4 4 1 4 3 5 5 1]
target = [0 2 3 4 2 4 2 3 5 5]
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
计算准确度：
--------------------------------------------------
batch_size = 10
--------------------------------------------------
pred_top_k = tf.Tensor(
[[0 1 5 2 3]
[4 3 5 0 1]
[4 0 1 5 2]
[4 0 3 1 2]
[1 4 3 2 0]
[4 5 2 1 0]
[3 1 5 2 0]
[5 1 3 2 0]
[5 4 2 3 1]
[1 3 2 4 5]], shape=(10, 5), dtype=int32)
--------------------------------------------------
转置之后：pred_top_k = tf.Tensor(
[[0 4 4 4 1 4 3 5 5 1]
[1 3 0 0 4 5 1 1 4 3]
[5 5 1 3 3 2 5 3 2 2]
[2 0 5 1 2 1 2 2 3 4]
[3 1 2 2 0 0 0 0 1 5]], shape=(5, 10), dtype=int32)
--------------------------------------------------
target_ = tf.Tensor(
[[0 2 3 4 2 4 2 3 5 5]
[0 2 3 4 2 4 2 3 5 5]
[0 2 3 4 2 4 2 3 5 5]
[0 2 3 4 2 4 2 3 5 5]
[0 2 3 4 2 4 2 3 5 5]], shape=(5, 10), dtype=int32)
--------------------------------------------------
equal_result = tf.Tensor(
[[ True False False True False True False False True False]
[False False False False False False False False False False]
[False False False False False False False True False False]
[False False False False True False True False False False]
[False False False False False False False False False True]], shape=(5, 10), dtype=bool)
--------------------------------------------------
equal_count_sum = tf.Tensor(8.0, shape=(), dtype=float32)
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
top-5 acc: [80.0]
Process finished with exit code 0
2、Top-k 准确度
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
tf.random.set_seed(2467)
# output为神经网络输出值
# target为实际值
# topk为想输出的top_k的值
def accuracy(output, target, topk=(1,)):
print("计算准确度：")
maxk = max(topk)
print("\tmaxk = ", maxk)
print("\t", "-" * 50)
batch_size = target.shape[0]
print("\tbatch_size = ", batch_size)
print("\t", "-" * 50)
pred_top_k = tf.math.top_k(input=output, k=maxk).indices # 计算top_k的索引，也即预测值
print("\tpred_top_k = ", pred_top_k)
print("\t", "-" * 50)
pred_top_k = tf.transpose(pred_top_k, perm=[1, 0]) # 将索引值进行转置
print("\t转置之后：pred_top_k = ", pred_top_k)
print("\t", "-" * 50)
target_ = tf.broadcast_to(target, pred_top_k.shape) # 对target进行扩充,扩展成与top矩阵相同维度的tensor，方便比较
print("\ttarget_ = ", target_)
print("\t", "-" * 50)
correct = tf.equal(pred_top_k, target_) # 比较输出结果
print("\tcorrect = ", correct)
print("\t", "-" * 50)
res = []
for k in topk: # 输出不同的Top_k对应的准确度
# correct[:k] 取不同的top_k的数据
correct_k = tf.reduce_sum(tf.cast(correct[:k], dtype=tf.float32))
acc = float(correct_k * (100.0 / batch_size))
res.append(acc)
return res
output = tf.random.normal([10, 6]) # 生成一个六类的样本，样本数为10
output = tf.math.softmax(output, axis=1) # softmax可以使得六类的概率为1
print('output = \n', output.numpy())
print("-" * 200)
pred = tf.argmax(output, axis=1)
print('\nprob = ', pred.numpy())
# 假设分类目标标签为：
target = tf.constant([0, 2, 3, 4, 2, 4, 2, 3, 5, 5]) # 结果为【0~5】的10个样本
print('target = ', target.numpy())
print("-" * 200)
acc = accuracy(output, target, topk=(1, 2, 3, 4, 5, 6)) # 调用精确度计算函数
print("-" * 200)
print('\ntop-1-6 acc:', acc)
打印结果：
output =
[[0.25310278 0.21715644 0.16043882 0.13088997 0.04334084 0.19507109]
[0.05892418 0.04548917 0.00926314 0.14529602 0.66777605 0.07325139]
[0.09742809 0.08304427 0.07460099 0.04067177 0.626185 0.07806987]
[0.20478569 0.12294925 0.12010485 0.13751233 0.36418733 0.05046057]
[0.11872064 0.31072393 0.12530337 0.15528883 0.21325873 0.07670453]
[0.01519807 0.09672114 0.1460476 0.00934331 0.5649092 0.1677807 ]
[0.04199061 0.18141054 0.06647632 0.6006175 0.03198383 0.07752118]
[0.09226219 0.23460893 0.13022321 0.16295876 0.05362028 0.32632664]
[0.07019574 0.08611772 0.10912607 0.10521299 0.2152082 0.4141393 ]
[0.01882887 0.2659769 0.19122466 0.2410926 0.14920163 0.1336753 ]]
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
prob = [0 4 4 4 1 4 3 5 5 1]
target = [0 2 3 4 2 4 2 3 5 5]
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
计算准确度：
maxk = 6
--------------------------------------------------
batch_size = 10
--------------------------------------------------
pred_top_k = tf.Tensor(
[[0 1 5 2 3 4]
[4 3 5 0 1 2]
[4 0 1 5 2 3]
[4 0 3 1 2 5]
[1 4 3 2 0 5]
[4 5 2 1 0 3]
[3 1 5 2 0 4]
[5 1 3 2 0 4]
[5 4 2 3 1 0]
[1 3 2 4 5 0]], shape=(10, 6), dtype=int32)
--------------------------------------------------
转置之后：pred_top_k = tf.Tensor(
[[0 4 4 4 1 4 3 5 5 1]
[1 3 0 0 4 5 1 1 4 3]
[5 5 1 3 3 2 5 3 2 2]
[2 0 5 1 2 1 2 2 3 4]
[3 1 2 2 0 0 0 0 1 5]
[4 2 3 5 5 3 4 4 0 0]], shape=(6, 10), dtype=int32)
--------------------------------------------------
target_ = tf.Tensor(
[[0 2 3 4 2 4 2 3 5 5]
[0 2 3 4 2 4 2 3 5 5]
[0 2 3 4 2 4 2 3 5 5]
[0 2 3 4 2 4 2 3 5 5]
[0 2 3 4 2 4 2 3 5 5]
[0 2 3 4 2 4 2 3 5 5]], shape=(6, 10), dtype=int32)
--------------------------------------------------
correct = tf.Tensor(
[[ True False False True False True False False True False]
[False False False False False False False False False False]
[False False False False False False False True False False]
[False False False False True False True False False False]
[False False False False False False False False False True]
[False True True False False False False False False False]], shape=(6, 10), dtype=bool)
--------------------------------------------------
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
top-1-6 acc: [40.0, 40.0, 50.0, 70.0, 80.0, 100.0]
Process finished with exit code 0
[TensorFlow 2] 张量排序
TensorFlow2.0（3）：张量排序、最大最小值
TensorFlow高阶操作之张量排序
tensorflow张量排序
TensorFlow2.0（3）：张量排序、最大最小值
tensorflow张量排序(示例代码)
张量排序
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