TensorFlow2-高阶操作（七）：where（采集元素）【从1个Tensor中采集元素】【从2个Tensor中采集元素：where(cond, A, B)】

短命女 2023-09-29 10:51 2阅读 0赞

## 一、where：采集元素 ##

### 1、从1个Tensor中采集元素【indices=where(mask)、gather\_nd(input, indices)】 ###

![在这里插入图片描述][watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBAdTAxMzI1MDg2MQ_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center]

#### 1.1 使用boolean\_mask采集元素 ####

import tensorflow as tf
    
    a = tf.convert_to_tensor(
        [[0.79134136, 0.09345922, -0.7822895],
         [1.9430199, -0.2962239, -1.1451387],
         [0.35126936, 1.0099757, 0.67769486]])
    print("a = \n", a)
    print("-" * 200)
    
    mask = a > 0
    print("mask = \n", mask)
    print("-" * 100)
    
    b = tf.boolean_mask(a, mask)
    print("b = \n", b)
    print("-" * 200)

打印结果：

a = 
     tf.Tensor(
    [[ 0.79134136  0.09345922 -0.7822895 ]
     [ 1.9430199  -0.2962239  -1.1451387 ]
     [ 0.35126936  1.0099757   0.67769486]], shape=(3, 3), dtype=float32)
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    mask = 
     tf.Tensor(
    [[ True  True False]
     [ True False False]
     [ True  True  True]], shape=(3, 3), dtype=bool)
    ----------------------------------------------------------------------------------------------------
    b = 
     tf.Tensor([0.79134136 0.09345922 1.9430199  0.35126936 1.0099757  0.67769486], shape=(6,), dtype=float32)
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    
    Process finished with exit code 0

#### 1.2 使用where采集元素 ####

import tensorflow as tf
    
    a = tf.convert_to_tensor(
        [[0.79134136, 0.09345922, -0.7822895],
         [1.9430199, -0.2962239, -1.1451387],
         [0.35126936, 1.0099757, 0.67769486]])
    
    print("a = \n", a)
    print("-" * 200)
    
    mask = a > 0
    print("mask = a > 0 = \n", mask)
    print("-" * 100)
    
    indices = tf.where(mask)
    print("indices = tf.where(mask) = \n", indices)
    print("-" * 100)
    
    d = tf.gather_nd(a, indices)
    print("d = tf.gather_nd(a, indices) = \n", d)
    print("-" * 200)

打印结果：

a = 
     tf.Tensor(
    [[ 0.79134136  0.09345922 -0.7822895 ]
     [ 1.9430199  -0.2962239  -1.1451387 ]
     [ 0.35126936  1.0099757   0.67769486]], shape=(3, 3), dtype=float32)
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    mask = a > 0 = 
     tf.Tensor(
    [[ True  True False]
     [ True False False]
     [ True  True  True]], shape=(3, 3), dtype=bool)
    ----------------------------------------------------------------------------------------------------
    indices = tf.where(mask) = 
     tf.Tensor(
    [[0 0]
     [0 1]
     [1 0]
     [2 0]
     [2 1]
     [2 2]], shape=(6, 2), dtype=int64)
    ----------------------------------------------------------------------------------------------------
    d = tf.gather_nd(a, indices) = 
     tf.Tensor([0.79134136 0.09345922 1.9430199  0.35126936 1.0099757  0.67769486], shape=(6,), dtype=float32)
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    
    Process finished with exit code 0

### 2、从2个Tensor中采集元素【where(cond, A, B)】 ###

![在这里插入图片描述][watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBAdTAxMzI1MDg2MQ_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center 1]

where(condition, x=None, y=None, name=None)

*  tf.where 通过boolean矩阵的 true or false 对候选条件下的两个矩阵进行element选取
 *  这里true就选x中的元素，false就选y中的元素

import tensorflow as tf
    
    a = tf.where([[True, False], [False, True]], x=[[1, 2], [3, 4]], y=[[5, 6], [7, 8]])
    
    print("a = \n", a)

打印结果：

a = 
     tf.Tensor(
    [[1 6]
     [7 4]], shape=(2, 2), dtype=int32)

--------------------

参考资料：  
[2020-06-05-tensorflow2-tf.where说明和例子][2020-06-05-tensorflow2-tf.where]  
[TensorFlow的tf.where函数详解与例子][TensorFlow_tf.where]  
[TensorFlow函数：tf.where][TensorFlow_tf.where 1]

[watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBAdTAxMzI1MDg2MQ_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center]: https://img-blog.csdnimg.cn/ddf0f25dc4d3472a8b97d889634c539b.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAdTAxMzI1MDg2MQ==,size_20,color_FFFFFF,t_70,g_se,x_16#pic_center
[watermark_type_d3F5LXplbmhlaQ_shadow_50_text_Q1NETiBAdTAxMzI1MDg2MQ_size_20_color_FFFFFF_t_70_g_se_x_16_pic_center 1]: https://img-blog.csdnimg.cn/9cb5c2f166cd482a84f64d250db1faed.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAdTAxMzI1MDg2MQ==,size_20,color_FFFFFF,t_70,g_se,x_16#pic_center
[2020-06-05-tensorflow2-tf.where]: https://www.jianshu.com/p/0915bd2b4f15
[TensorFlow_tf.where]: http://t.zoukankan.com/yaos-p-12745823.html
[TensorFlow_tf.where 1]: https://blog.csdn.net/A_a_ron/article/details/79048446