TensorFlow 索引与切片

Reference


Basic indexing

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
arr = tf.range(60)
t1 = tf.reshape(arr, [3, 4, 5])
print(t1) # tf.Tensor(
# [[[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]]
#
# [[20 21 22 23 24]
# [25 26 27 28 29]
# [30 31 32 33 34]
# [35 36 37 38 39]]
#
# [[40 41 42 43 44]
# [45 46 47 48 49]
# [50 51 52 53 54]
# [55 56 57 58 59]]], shape=(3, 4, 5), dtype=int32)
print(t1[0]) # tf.Tensor(
# [[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]], shape=(4, 5), dtype=int32)
print(t1[0][0]) # tf.Tensor([0 1 2 3 4], shape=(5,), dtype=int32)
print(t1[0][0][0]) # tf.Tensor(0, shape=(), dtype=int32)

numpy-style indexing

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
arr = tf.range(60)
t1 = tf.reshape(arr, [3, 4, 5])
print(t1) # tf.Tensor(
# [[[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]]
#
# [[20 21 22 23 24]
# [25 26 27 28 29]
# [30 31 32 33 34]
# [35 36 37 38 39]]
#
# [[40 41 42 43 44]
# [45 46 47 48 49]
# [50 51 52 53 54]
# [55 56 57 58 59]]], shape=(3, 4, 5), dtype=int32)
print(t1[0]) # tf.Tensor(
# [[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]], shape=(4, 5), dtype=int32)
print(t1[0, 0]) # tf.Tensor([0 1 2 3 4], shape=(5,), dtype=int32)
print(t1[0, 0, 0]) # tf.Tensor(0, shape=(), dtype=int32)

截取

1
2
3
4
5
6
7
8
9
10
t = tf.range(10)
t1 = t[2:]
t2 = t[-2:]
t3 = t[:2]
t4 = t[:-2]
print(t) # tf.Tensor([0 1 2 3 4 5 6 7 8 9], shape=(10,), dtype=int32)
print(t1) # tf.Tensor([2 3 4 5 6 7 8 9], shape=(8,), dtype=int32)
print(t2) # tf.Tensor([8 9], shape=(2,), dtype=int32)
print(t3) # tf.Tensor([0 1], shape=(2,), dtype=int32)
print(t4) # tf.Tensor([0 1 2 3 4 5 6 7], shape=(8,), dtype=int32)

indexing by :

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
arr = tf.range(60)
t = tf.reshape(arr, [3, 4, 5])
t1 = t[0, :, :]
t2 = t[:, 0, :]
t3 = t[:, :, 0]
print(t) # tf.Tensor(
# [[[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]]
#
# [[20 21 22 23 24]
# [25 26 27 28 29]
# [30 31 32 33 34]
# [35 36 37 38 39]]
#
# [[40 41 42 43 44]
# [45 46 47 48 49]
# [50 51 52 53 54]
# [55 56 57 58 59]]], shape=(3, 4, 5), dtype=int32)
print(t1) # tf.Tensor(
# [[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]], shape=(4, 5), dtype=int32)
print(t2) # tf.Tensor(
# [[ 0 1 2 3 4]
# [20 21 22 23 24]
# [40 41 42 43 44]], shape=(3, 5), dtype=int32)
print(t3) # tf.Tensor(
# [[ 0 5 10 15]
# [20 25 30 35]
# [40 45 50 55]], shape=(3, 4), dtype=int32)

indexing by ::

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
arr = tf.range(60)
t = tf.reshape(arr, [3, 4, 5])
t1 = t[:, ::2, :]
t2 = t[0::2, :, :]
t3 = t[0, ::2, :]
print(t) # tf.Tensor(
# [[[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]]
#
# [[20 21 22 23 24]
# [25 26 27 28 29]
# [30 31 32 33 34]
# [35 36 37 38 39]]
#
# [[40 41 42 43 44]
# [45 46 47 48 49]
# [50 51 52 53 54]
# [55 56 57 58 59]]], shape=(3, 4, 5), dtype=int32)
print(t1) # tf.Tensor(
# [[[ 0 1 2 3 4]
# [10 11 12 13 14]]
#
# [[20 21 22 23 24]
# [30 31 32 33 34]]
#
# [[40 41 42 43 44]
# [50 51 52 53 54]]], shape=(3, 2, 5), dtype=int32)
print(t2) # tf.Tensor(
# [[[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]]
#
# [[40 41 42 43 44]
# [45 46 47 48 49]
# [50 51 52 53 54]
# [55 56 57 58 59]]], shape=(2, 4, 5), dtype=int32)
print(t3) # tf.Tensor(
# [[ 0 1 2 3 4]
# [10 11 12 13 14]], shape=(2, 5), dtype=int32)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
arr = tf.range(60)
t = tf.reshape(arr, [3, 4, 5])
t1 = t[0, ...]
t2 = t[..., 0]
t3 = t[0, 0, ...]
t4 = t[..., 0, 0]
print(t) # tf.Tensor(
# [[[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]]
#
# [[20 21 22 23 24]
# [25 26 27 28 29]
# [30 31 32 33 34]
# [35 36 37 38 39]]
#
# [[40 41 42 43 44]
# [45 46 47 48 49]
# [50 51 52 53 54]
# [55 56 57 58 59]]], shape=(3, 4, 5), dtype=int32)
print(t1) # tf.Tensor(
# [[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]], shape=(4, 5), dtype=int32)
print(t2) # tf.Tensor(
# [[ 0 5 10 15]
# [20 25 30 35]
# [40 45 50 55]], shape=(3, 4), dtype=int32)
print(t3) # tf.Tensor([0 1 2 3 4], shape=(5,), dtype=int32)
print(t4) # tf.Tensor([ 0 20 40], shape=(3,), dtype=int32)

gather

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
arr = tf.range(60)
t = tf.reshape(arr, [3, 4, 5])
t1 = tf.gather(t, axis=0, indices=[0, 2])
t2 = tf.gather(t, axis=0, indices=[0, 2, 1])
t3 = tf.gather(t, axis=1, indices=[0, 2, 1])
t4 = tf.gather(t, axis=1, indices=[0, 2, 1, 3])
t5 = tf.gather(t, axis=2, indices=[0, 2, 1, 4])
t6 = tf.gather(t, axis=2, indices=[0, 2, 1, 4, 3])
print(t1) # tf.Tensor(
# [[[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]]
#
# [[40 41 42 43 44]
# [45 46 47 48 49]
# [50 51 52 53 54]
# [55 56 57 58 59]]], shape=(2, 4, 5), dtype=int32)
print(t2) # tf.Tensor(
# [[[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]]
#
# [[40 41 42 43 44]
# [45 46 47 48 49]
# [50 51 52 53 54]
# [55 56 57 58 59]]
#
# [[20 21 22 23 24]
# [25 26 27 28 29]
# [30 31 32 33 34]
# [35 36 37 38 39]]], shape=(3, 4, 5), dtype=int32)
print(t3) # tf.Tensor(
# [[[ 0 1 2 3 4]
# [10 11 12 13 14]
# [ 5 6 7 8 9]]
#
# [[20 21 22 23 24]
# [30 31 32 33 34]
# [25 26 27 28 29]]
#
# [[40 41 42 43 44]
# [50 51 52 53 54]
# [45 46 47 48 49]]], shape=(3, 3, 5), dtype=int32)
print(t4) # tf.Tensor(
# [[[ 0 1 2 3 4]
# [10 11 12 13 14]
# [ 5 6 7 8 9]
# [15 16 17 18 19]]
#
# [[20 21 22 23 24]
# [30 31 32 33 34]
# [25 26 27 28 29]
# [35 36 37 38 39]]
#
# [[40 41 42 43 44]
# [50 51 52 53 54]
# [45 46 47 48 49]
# [55 56 57 58 59]]], shape=(3, 4, 5), dtype=int32)
print(t5) # tf.Tensor(
# [[[ 0 2 1 4]
# [ 5 7 6 9]
# [10 12 11 14]
# [15 17 16 19]]
#
# [[20 22 21 24]
# [25 27 26 29]
# [30 32 31 34]
# [35 37 36 39]]
#
# [[40 42 41 44]
# [45 47 46 49]
# [50 52 51 54]
# [55 57 56 59]]], shape=(3, 4, 4), dtype=int32)
print(t6) # tf.Tensor(
# [[[ 0 2 1 4 3]
# [ 5 7 6 9 8]
# [10 12 11 14 13]
# [15 17 16 19 18]]
#
# [[20 22 21 24 23]
# [25 27 26 29 28]
# [30 32 31 34 33]
# [35 37 36 39 38]]
#
# [[40 42 41 44 43]
# [45 47 46 49 48]
# [50 52 51 54 53]
# [55 57 56 59 58]]], shape=(3, 4, 5), dtype=int32)

gather_nd

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
arr = tf.range(60)
t = tf.reshape(arr, [3, 4, 5])
t1 = tf.gather_nd(t, [0])
t2 = tf.gather_nd(t, [0, 1])
t3 = tf.gather_nd(t, [0, 1, 2])
t4 = tf.gather_nd(t, [[0, 1, 2]])
t5 = tf.gather_nd(t, [[0, 0], [1, 1]])
t6 = tf.gather_nd(t, [[0, 0], [1, 1], [2, 2]])
t7 = tf.gather_nd(t, [[0, 0, 0], [1, 1, 1], [2, 2, 2]])
t8 = tf.gather_nd(t, [[[0, 0, 0], [1, 1, 1], [2, 2, 2]]])
print(t) # tf.Tensor(
# [[[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]]
#
# [[20 21 22 23 24]
# [25 26 27 28 29]
# [30 31 32 33 34]
# [35 36 37 38 39]]
#
# [[40 41 42 43 44]
# [45 46 47 48 49]
# [50 51 52 53 54]
# [55 56 57 58 59]]], shape=(3, 4, 5), dtype=int32)
print(t1) # tf.Tensor(
# [[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]], shape=(4, 5), dtype=int32)
print(t2) # tf.Tensor([5 6 7 8 9], shape=(5,), dtype=int32)
print(t3) # tf.Tensor(7, shape=(), dtype=int32)
print(t4) # tf.Tensor([7], shape=(1,), dtype=int32)
print(t5) # tf.Tensor(
# [[ 0 1 2 3 4]
# [25 26 27 28 29]], shape=(2, 5), dtype=int32)
print(t6) # tf.Tensor(
# [[ 0 1 2 3 4]
# [25 26 27 28 29]
# [50 51 52 53 54]], shape=(3, 5), dtype=int32)
print(t7) # tf.Tensor([ 0 26 52], shape=(3,), dtype=int32)
print(t8) # tf.Tensor([[ 0 26 52]], shape=(1, 3), dtype=int32)

boolean_mask

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
arr = tf.range(60)
t = tf.reshape(arr, [3, 4, 5])
t1 = tf.boolean_mask(t, mask=[True, False, True])
t2 = tf.boolean_mask(t, mask=[True, False, True, True], axis=1)
t3 = tf.boolean_mask(t, mask=[[True, False, True, True], [True, False, False, True], [False, True, False, True]])
print(t) # tf.Tensor(
# [[[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]]
#
# [[20 21 22 23 24]
# [25 26 27 28 29]
# [30 31 32 33 34]
# [35 36 37 38 39]]
#
# [[40 41 42 43 44]
# [45 46 47 48 49]
# [50 51 52 53 54]
# [55 56 57 58 59]]], shape=(3, 4, 5), dtype=int32)
print(t1) # tf.Tensor(
# [[[ 0 1 2 3 4]
# [ 5 6 7 8 9]
# [10 11 12 13 14]
# [15 16 17 18 19]]
#
# [[40 41 42 43 44]
# [45 46 47 48 49]
# [50 51 52 53 54]
# [55 56 57 58 59]]], shape=(2, 4, 5), dtype=int32)
print(t2) # tf.Tensor(
# [[[ 0 1 2 3 4]
# [10 11 12 13 14]
# [15 16 17 18 19]]
#
# [[20 21 22 23 24]
# [30 31 32 33 34]
# [35 36 37 38 39]]
#
# [[40 41 42 43 44]
# [50 51 52 53 54]
# [55 56 57 58 59]]], shape=(3, 3, 5), dtype=int32)
print(t3) # tf.Tensor(
# [[ 0 1 2 3 4]
# [10 11 12 13 14]
# [15 16 17 18 19]
# [20 21 22 23 24]
# [35 36 37 38 39]
# [45 46 47 48 49]
# [55 56 57 58 59]], shape=(7, 5), dtype=int32)

本文标题:TensorFlow 索引与切片

文章作者:魏超

发布时间:2019年05月09日 - 09:05

最后更新:2019年05月20日 - 09:05

原始链接:http://www.weichao.io/2019/05/09/TensorFlow-索引与切片/

许可协议: 署名-非商业性使用-禁止演绎 4.0 国际 转载请保留原文链接及作者。

---------------------本文结束---------------------