张量排序 - 好文

目录

* Outline <https://www.cnblogs.com/nickchen121/p/10852639.html#outline>
* Sort/argsort
<https://www.cnblogs.com/nickchen121/p/10852639.html#sortargsort>
* 一维 <https://www.cnblogs.com/nickchen121/p/10852639.html#一维>
* 二维 <https://www.cnblogs.com/nickchen121/p/10852639.html#二维>
* Top_k <https://www.cnblogs.com/nickchen121/p/10852639.html#top_k>
* Top_one <https://www.cnblogs.com/nickchen121/p/10852639.html#top_one>
* Top-k accuracy
<https://www.cnblogs.com/nickchen121/p/10852639.html#top-k-accuracy>
* 示例 <https://www.cnblogs.com/nickchen121/p/10852639.html#示例>
Outline

*
Sort/argsort

*
Topk

*
Top-5 Acc.

Sort/argsort

一维
import tensorflow as tf a = tf.random.shuffle(tf.range(5)) a <tf.Tensor:
id=59, shape=(5,), dtype=int32, numpy=array([4, 0, 3, 2, 1], dtype=int32)>
tf.sort(a, direction='DESCENDING') <tf.Tensor: id=69, shape=(5,), dtype=int32,
numpy=array([4, 3, 2, 1, 0], dtype=int32)> # 返回索引 tf.argsort(a,
direction='DESCENDING') <tf.Tensor: id=81, shape=(5,), dtype=int32,
numpy=array([0, 2, 3, 4, 1], dtype=int32)> idx = tf.argsort(a,
direction='DESCENDING') tf.gather(a, idx) <tf.Tensor: id=94, shape=(5,),
dtype=int32, numpy=array([4, 3, 2, 1, 0], dtype=int32)>
二维
a = tf.random.uniform([3, 3], maxval=10, dtype=tf.int32) a <tf.Tensor: id=99,
shape=(3, 3), dtype=int32, numpy= array([[1, 9, 4], [2, 1, 4], [3, 6, 0]],
dtype=int32)> tf.sort(a) <tf.Tensor: id=112, shape=(3, 3), dtype=int32, numpy=
array([[1, 4, 9], [1, 2, 4], [0, 3, 6]], dtype=int32)> tf.sort(a,
direction='DESCENDING') <tf.Tensor: id=122, shape=(3, 3), dtype=int32, numpy=
array([[9, 4, 1], [4, 2, 1], [6, 3, 0]], dtype=int32)> idx = tf.argsort(a) idx
<tf.Tensor: id=146, shape=(3, 3), dtype=int32, numpy= array([[0, 2, 1], [1, 0,
2], [2, 0, 1]], dtype=int32)>
Top_k

* Only return top-k values and indices
Top_one
a <tf.Tensor: id=99, shape=(3, 3), dtype=int32, numpy= array([[1, 9, 4], [2,
1, 4], [3, 6, 0]], dtype=int32)> # 返回前2个值 res = tf.math.top_k(a, 2) res
TopKV2(values=<tf.Tensor: id=160, shape=(3, 2), dtype=int32, numpy= array([[9,
4], [4, 2], [6, 3]], dtype=int32)>, indices=<tf.Tensor: id=161, shape=(3, 2),
dtype=int32, numpy= array([[1, 2], [2, 0], [1, 0]], dtype=int32)>) res.values
<tf.Tensor: id=160, shape=(3, 2), dtype=int32, numpy= array([[9, 4], [4, 2],
[6, 3]], dtype=int32)> res.indices <tf.Tensor: id=161, shape=(3, 2),
dtype=int32, numpy= array([[1, 2], [2, 0], [1, 0]], dtype=int32)>
Top-k accuracy

*
Prob:[0.1,0.2,0.3,0.4]

*
Lable:[2]

* Only consider top-1 prediction:[3]
* Only consider top-2 prediction:[3,2]
*
Only consider top-3 prediction:[3,2,1]
prob = tf.constant([[0.1, 0.2, 0.7], [0.2, 0.7, 0.1]]) target =
tf.constant([2, 0]) # 概率最大的索引在最前面 k_b = tf.math.top_k(prob, 3).indices k_b
<tf.Tensor: id=190, shape=(2, 3), dtype=int32, numpy= array([[2, 1, 0], [1, 0,
2]], dtype=int32)> k_b = tf.transpose(k_b, [1, 0]) k_b <tf.Tensor: id=193,
shape=(3, 2), dtype=int32, numpy= array([[2, 1], [1, 0], [0, 2]], dtype=int32)>
# 对真实值broadcast，与prod比较 target = tf.broadcast_to(target, [3, 2]) target
<tf.Tensor: id=196, shape=(3, 2), dtype=int32, numpy= array([[2, 0], [2, 0],
[2, 0]], dtype=int32)>
示例
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 # 10个样本6类 output = tf.random.normal([10, 6]) # 使得所有样本的概率加起来为1 output
= tf.math.softmax(output, axis=1) # 10个样本对应的标记 target = tf.random.uniform([10],
maxval=6, dtype=tf.int32) print(f'prob: {output.numpy()}') pred =
tf.argmax(output, axis=1) print(f'pred: {pred.numpy()}') print(f'label:
{target.numpy()}') acc = accuracy(output, target, topk=(1, 2, 3, 4, 5, 6))
print(f'top-1-6 acc: {acc}') prob: [[0.12232917 0.18645659 0.27771464
0.17322136 0.14854735 0.09173083] [0.02338449 0.01026637 0.11773597 0.69083494
0.03814701 0.11963127] [0.05774692 0.1926369 0.49359822 0.10262781 0.10738047
0.0460096 ] [0.21298195 0.02826484 0.1813868 0.06380058 0.06848615 0.44507968]
[0.01364106 0.16782394 0.08621352 0.22500433 0.19081964 0.31649753] [0.02917767
0.15526605 0.6310118 0.11471876 0.05473462 0.0150911 ] [0.03684716 0.15286008
0.11792535 0.47401306 0.05833342 0.160021 ] [0.32859987 0.17415446 0.07394216
0.22221863 0.07559296 0.12549189] [0.02662764 0.5529567 0.06995299 0.02131662
0.08664025 0.2425058 ] [0.10253917 0.10178788 0.21553555 0.12878521 0.3788466
0.07250563]] pred: [2 3 2 5 5 2 3 0 1 4] label: [3 4 3 0 4 0 3 2 1 4] top-1-6
acc: [0.30000001192092896, 0.4000000059604645, 0.6000000238418579,
0.800000011920929, 0.8999999761581421, 1.0]

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