张量排序
TensorFlow2教程完整教程目录（更有python、go、pytorch、tensorflow、爬虫、人工智能教学等着你）：https://www.cnblogs.com/nickchen121/p/10840284.html
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]
