Numpy实现激活函数

import numpy as np


# Collection of activation functions
# Reference: https://en.wikipedia.org/wiki/Activation_function

class Sigmoid():
    def __call__(self, x):
        return 1 / (1 + np.exp(-x))

    def gradient(self, x):
        return self.__call__(x) * (1 - self.__call__(x))


class Softmax():
    def __call__(self, x):
        e_x = np.exp(x - np.max(x, axis=-1, keepdims=True))
        return e_x / np.sum(e_x, axis=-1, keepdims=True)

    def gradient(self, x):
        p = self.__call__(x)
        return p * (1 - p)


class TanH():
    def __call__(self, x):
        return 2 / (1 + np.exp(-2 * x)) - 1

    def gradient(self, x):
        return 1 - np.power(self.__call__(x), 2)


class ReLU():
    def __call__(self, x):
        return np.where(x >= 0, x, 0)

    def gradient(self, x):
        return np.where(x >= 0, 1, 0)


class LeakyReLU():
    def __init__(self, alpha=0.2):
        self.alpha = alpha

    def __call__(self, x):
        return np.where(x >= 0, x, self.alpha * x)

    def gradient(self, x):
        return np.where(x >= 0, 1, self.alpha)


class ELU(object):
    def __init__(self, alpha=0.1):
        self.alpha = alpha

    def __call__(self, x):
        return np.where(x >= 0.0, x, self.alpha * (np.exp(x) - 1))

    def gradient(self, x):
        return np.where(x >= 0.0, 1, self.__call__(x) + self.alpha)


class SELU():
    # Reference : https://arxiv.org/abs/1706.02515,
    # https://github.com/bioinf-jku/SNNs/blob/master/SelfNormalizingNetworks_MLP_MNIST.ipynb
    def __init__(self):
        self.alpha = 1.6732632423543772848170429916717
        self.scale = 1.0507009873554804934193349852946

    def __call__(self, x):
        return self.scale * np.where(x >= 0.0, x, self.alpha * (np.exp(x) - 1))

    def gradient(self, x):
        return self.scale * np.where(x >= 0.0, 1, self.alpha * np.exp(x))


class SoftPlus(object):
    def __call__(self, x):
        return np.log(1 + np.exp(x))

    def gradient(self, x):
        return 1 / (1 + np.exp(-x))


class Swish(object):
    def __init__(self, b):
        self.b = b

    def __call__(self, x):
        return x * (np.exp(self.b * x) / (np.exp(self.b * x) + 1))

    def gradient(self, x):
        return self.b * x / (1 + np.exp(-self.b * x)) + (1 / (1 + np.exp(-self.b * x)))(
            1 - self.b * (x / (1 + np.exp(-self.b * x))))


def sech(x):
    """sech函数"""
    return 2 / (np.exp(x) + np.exp(-x))


def sigmoid(x):
    """sigmoid函数"""
    return 1 / (1 + np.exp(-x))


def soft_plus(x):
    """softplus函数"""
    return np.log(1 + np.exp(x))


def tan_h(x):
    """tanh函数"""
    return (np.exp(x) - np.exp(-x)) / (np.exp(x) + np.exp(-x))


class Mish:

    def __call__(self, x):
        return x * tan_h(soft_plus(x))

    def gradient(self, x):
        return sech(soft_plus(x)) * sech(soft_plus(x)) * x * sigmoid(x) + tan_h(soft_plus(x))

class SILU(object):

    def __call__(self, x):
        return x * sigmoid(x)

    def gradient(self, x):
        return self.__call__(x) + sigmoid(x) * (1 - self.__call__(x))



  

 

  
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
  
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
 

文章来源: wanghao.blog.csdn.net，作者：AI浩，版权归原作者所有，如需转载，请联系作者。

原文链接：wanghao.blog.csdn.net/article/details/120321801