高阶操作

目录

* Outline <https://www.cnblogs.com/nickchen121/p/10864401.html#outline>
* Where <https://www.cnblogs.com/nickchen121/p/10864401.html#where>
* where(tensor)
<https://www.cnblogs.com/nickchen121/p/10864401.html#wheretensor>
* where(cond,A,B)
<https://www.cnblogs.com/nickchen121/p/10864401.html#wherecondab>
* scatter_nd <https://www.cnblogs.com/nickchen121/p/10864401.html#scatter_nd>
* 一维 <https://www.cnblogs.com/nickchen121/p/10864401.html#一维>
* 二维 <https://www.cnblogs.com/nickchen121/p/10864401.html#二维>
* meshgrid <https://www.cnblogs.com/nickchen121/p/10864401.html#meshgrid>
* Points <https://www.cnblogs.com/nickchen121/p/10864401.html#points>
* numpy实现 <https://www.cnblogs.com/nickchen121/p/10864401.html#numpy实现>
* tensorflow2实现
<https://www.cnblogs.com/nickchen121/p/10864401.html#tensorflow2实现>
Outline

*
where

*
scatter_nd

*
meshgrid

Where

where(tensor)

* where获得以下表格中True的位置
1 2 3
True False False
False True False
False False True import tensorflow as tf a = tf.random.normal([3, 3]) a
<tf.Tensor: id=11, shape=(3, 3), dtype=float32, numpy= array([[-0.02527909,
-0.09084062, 0.34427297], [-0.45223615, 1.1085868 , -1.9480664 ], [-2.3520288 ,
-1.8698558 , -0.30862013]], dtype=float32)> mask = a > 0 mask <tf.Tensor:
id=16, shape=(3, 3), dtype=bool, numpy= array([[False, False, True], [False,
True, False], [False, False, False]])> # 为True元素的值 tf.boolean_mask(a, mask)
<tf.Tensor: id=44, shape=(2,), dtype=float32, numpy=array([0.34427297,
1.1085868 ], dtype=float32)> # 为True元素，即>0的元素的索引 indices = tf.where(mask)
indices <tf.Tensor: id=47, shape=(2, 2), dtype=int64, numpy= array([[0, 2], [1,
1]])> # 取回>0的值 tf.gather_nd(a, indices) <tf.Tensor: id=49, shape=(2,),
dtype=float32, numpy=array([0.34427297, 1.1085868 ], dtype=float32)>
where(cond,A,B)
mask <tf.Tensor: id=16, shape=(3, 3), dtype=bool, numpy= array([[False, False,
True], [False, True, False], [False, False, False]])> A = tf.ones([3, 3]) B =
tf.zeros([3, 3]) # True的元素会从A中选值，False的元素会从B中选值 tf.where(mask, A, B)
<tf.Tensor: id=61, shape=(3, 3), dtype=float32, numpy= array([[0., 0., 1.],
[0., 1., 0.], [0., 0., 0.]], dtype=float32)>
scatter_nd

* tf.scatter_nd(
* indices,
* updates,
* shape)
一维


indices = tf.constant([[4], [3], [1], [7]]) updates = tf.constant([9, 10, 11,
12]) shape = tf.constant([8]) # 把updates按照indices的索引放在底板shape上
tf.scatter_nd(indices, updates, shape) <tf.Tensor: id=71, shape=(8,),
dtype=int32, numpy=array([ 0, 11, 0, 10, 9, 0, 0, 12], dtype=int32)>
二维


indices = tf.constant([[0], [2]]) updates = tf.constant([ [[5, 5, 5, 5], [6,
6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7,
7], [8, 8, 8, 8]], ]) updates.shape TensorShape([2, 4, 4]) shape =
tf.constant([4, 4, 4]) tf.scatter_nd(indices, updates, shape) <tf.Tensor:
id=76, shape=(4, 4, 4), dtype=int32, numpy= array([[[5, 5, 5, 5], [6, 6, 6, 6],
[7, 7, 7, 7], [8, 8, 8, 8]], [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0,
0, 0]], [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[0, 0, 0,
0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]], dtype=int32)>
meshgrid

* [-2,-2]
* [-1,-2]
* [0,-2]
* [-2,-2]
* [-1,-1]
* ...
* [2,2]


Points

* [y,x,w]
* [5,5,2]
* [N,2]


numpy实现
import numpy as np points = [] for y in np.linspace(-2, 2, 5): for x in
np.linspace(-2, 2, 5): points.append([x, y]) np.array(points) array([[-2.,
-2.], [-1., -2.], [ 0., -2.], [ 1., -2.], [ 2., -2.], [-2., -1.], [-1., -1.], [
0., -1.], [ 1., -1.], [ 2., -1.], [-2., 0.], [-1., 0.], [ 0., 0.], [ 1., 0.], [
2., 0.], [-2., 1.], [-1., 1.], [ 0., 1.], [ 1., 1.], [ 2., 1.], [-2., 2.],
[-1., 2.], [ 0., 2.], [ 1., 2.], [ 2., 2.]])
tensorflow2实现
y = tf.linspace(-2., 2, 5) y <tf.Tensor: id=81, shape=(5,), dtype=float32,
numpy=array([-2., -1., 0., 1., 2.], dtype=float32)> x = tf.linspace(-2., 2, 5) x
<tf.Tensor: id=86, shape=(5,), dtype=float32, numpy=array([-2., -1., 0., 1.,
2.], dtype=float32)> points_x, points_y = tf.meshgrid(x, y) points_x.shape
TensorShape([5, 5]) points_x <tf.Tensor: id=130, shape=(5, 5), dtype=float32,
numpy= array([[-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.], [-2., -1., 0.,
1., 2.], [-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.]], dtype=float32)>
points_y <tf.Tensor: id=131, shape=(5, 5), dtype=float32, numpy= array([[-2.,
-2., -2., -2., -2.], [-1., -1., -1., -1., -1.], [ 0., 0., 0., 0., 0.], [ 1.,
1., 1., 1., 1.], [ 2., 2., 2., 2., 2.]], dtype=float32)> points =
tf.stack([points_x, points_y], axis=2) points <tf.Tensor: id=135, shape=(5, 5,
2), dtype=float32, numpy= array([[[-2., -2.], [-1., -2.], [ 0., -2.], [ 1.,
-2.], [ 2., -2.]], [[-2., -1.], [-1., -1.], [ 0., -1.], [ 1., -1.], [ 2.,
-1.]], [[-2., 0.], [-1., 0.], [ 0., 0.], [ 1., 0.], [ 2., 0.]], [[-2., 1.],
[-1., 1.], [ 0., 1.], [ 1., 1.], [ 2., 1.]], [[-2., 2.], [-1., 2.], [ 0., 2.],
[ 1., 2.], [ 2., 2.]]], dtype=float32)>

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