【599】keras.layers 里面 Multiply、multiply & Add、add 的区别

　　再看 Attention U-Net 源码的时候，注意到了有 keras.layers 里面有 Multiply 和 multiply 两个方法

　　它们可以实现相同的效果，但是语法稍有不同

# 按照图层的模式处理
Multiply()([m1, m2])

# 相当于一个函数操作
multiply([m1, m2])

　　另外可以实现 broadcast 操作，但是第 0 维必须为相同的数字，可以设想为样本数量是不变的，第 1 维可以有差别

　　举例

from keras.layers import Multiply, multiply, Add, add

import numpy as np 

a = np.arange(25).reshape(5, 1, 5)
a 

[out]

array([[[ 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]]])

b = np.arange(50, 150).reshape(5, 4, 5)
b 

[out]

array([[[ 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,  91,  92,  93,  94],
        [ 95,  96,  97,  98,  99],
        [100, 101, 102, 103, 104],
        [105, 106, 107, 108, 109]],

       [[110, 111, 112, 113, 114],
        [115, 116, 117, 118, 119],
        [120, 121, 122, 123, 124],
        [125, 126, 127, 128, 129]],

       [[130, 131, 132, 133, 134],
        [135, 136, 137, 138, 139],
        [140, 141, 142, 143, 144],
        [145, 146, 147, 148, 149]]])

Multiply()([a, b]) 
# Multiply()([b, a]) 
# 一样的效果
# multiply([a, b]) 
# multiply([b, a]) 

[out]

<tf.Tensor: shape=(5, 4, 5), dtype=int64, numpy=
array([[[   0,   51,  104,  159,  216],
        [   0,   56,  114,  174,  236],
        [   0,   61,  124,  189,  256],
        [   0,   66,  134,  204,  276]],

       [[ 350,  426,  504,  584,  666],
        [ 375,  456,  539,  624,  711],
        [ 400,  486,  574,  664,  756],
        [ 425,  516,  609,  704,  801]],

       [[ 900, 1001, 1104, 1209, 1316],
        [ 950, 1056, 1164, 1274, 1386],
        [1000, 1111, 1224, 1339, 1456],
        [1050, 1166, 1284, 1404, 1526]],

       [[1650, 1776, 1904, 2034, 2166],
        [1725, 1856, 1989, 2124, 2261],
        [1800, 1936, 2074, 2214, 2356],
        [1875, 2016, 2159, 2304, 2451]],

       [[2600, 2751, 2904, 3059, 3216],
        [2700, 2856, 3014, 3174, 3336],
        [2800, 2961, 3124, 3289, 3456],
        [2900, 3066, 3234, 3404, 3576]]])>

Add()([a, b]) 
# Add()([b, a]) 
# 一样的效果
# add([a, b]) 
# add([b, a]) 

[out]

<tf.Tensor: shape=(5, 4, 5), dtype=int64, numpy=
array([[[ 50,  52,  54,  56,  58],
        [ 55,  57,  59,  61,  63],
        [ 60,  62,  64,  66,  68],
        [ 65,  67,  69,  71,  73]],

       [[ 75,  77,  79,  81,  83],
        [ 80,  82,  84,  86,  88],
        [ 85,  87,  89,  91,  93],
        [ 90,  92,  94,  96,  98]],

       [[100, 102, 104, 106, 108],
        [105, 107, 109, 111, 113],
        [110, 112, 114, 116, 118],
        [115, 117, 119, 121, 123]],

       [[125, 127, 129, 131, 133],
        [130, 132, 134, 136, 138],
        [135, 137, 139, 141, 143],
        [140, 142, 144, 146, 148]],

       [[150, 152, 154, 156, 158],
        [155, 157, 159, 161, 163],
        [160, 162, 164, 166, 168],
        [165, 167, 169, 171, 173]]])>

　　对于 Attention U-Net 实现图如下：

【599】keras.layers 里面 Multiply、multiply & Add、add 的区别