C＃中的激活函数列表

我可以在数学中找到激活函数的列表，但不能在代码中找到。 所以我想这应该是代码中这样一个列表的正确位置，如果有的话。 从这两个链接中的算法翻译开始： https ： //en.wikipedia.org/wiki/Activation_function https://stats.stackexchange.com/questions/115258/comprehensive-list-of-activation-functions-in -neural的网络与-利弊，利弊

目标是通过UI轻松访问Activation类（包含函数及其派生类）。

编辑：我的尝试

using UnityEngine; using System.Collections; using System; ///
 ///Activation Functions from: ///https://en.wikipedia.org/wiki/Activation_function ///https://stats.stackexchange.com/questions/115258/comprehensive-list-of-activation-functions-in-neural-networks-with-pros-cons ///D infront means the Deravitive of the function ///x is the input of one perceptron. a is the alpha value sometimes needed. ///
 [System.Serializable] public class Activation { public ActivationType activationType; public Activation(ActivationType type) { activationType = type; } public double AFunction(double x) { switch(activationType) { case ActivationType.Identity: return Identity(x); case ActivationType.BinaryStep: return BinaryStep(x); case ActivationType.Logistic: return Logistic(x); case ActivationType.Tanh: return Tanh(x); case ActivationType.ArcTan: return ArcTan(x); case ActivationType.ReLU: return ReLU(x); case ActivationType.SoftPlus: return SoftPlus(x); case ActivationType.BentIdentity: return BentIdentity(x); case ActivationType.Sinusoid: return Sinusoid(x); case ActivationType.Sinc: return Sinc(x); case ActivationType.Gaussian: return Gaussian(x); case ActivationType.Bipolar: return Bipolar(x); case ActivationType.BipolarSigmoid: return BipolarSigmoid(x); } return 0; } public double ActivationDerivative(double x) { switch(activationType) { case ActivationType.Logistic: return DLogistic(x); case ActivationType.Tanh: return DTanh(x); case ActivationType.ArcTan: return DArcTan(x); case ActivationType.ReLU: return DReLU(x); case ActivationType.SoftPlus: return DSoftPlus(x); case ActivationType.BentIdentity: return DBentIdentity(x); case ActivationType.Sinusoid: return DSinusoid(x); case ActivationType.Sinc: return DSinc(x); case ActivationType.Gaussian: return DGaussian(x); case ActivationType.BipolarSigmoid: return DBipolarSigmoid(x); } return 0; } public double AFunction(double x, double a) { switch(activationType) { case ActivationType.PReLU: return PReLU(x,a); case ActivationType.ELU: return ELU(x,a); } return 0; } public double ActivationDerivative(double x, double a) { switch(activationType) { case ActivationType.PReLU: return DPReLU(x,a); case ActivationType.ELU: return DELU(x,a); } return 0; } public double Identity(double x) { return x; } public double BinaryStep(double x) { return x < 0 ? 0 : 1; } public double Logistic(double x) { return 1/(1+Math.Pow(Math.E,-x)); } public double DLogistic(double x) { return Logistic(x)*(1-Logistic(x)); } public double Tanh(double x) { return 2/(1+Math.Pow(Math.E, -(2*x)))-1; } public double DTanh(double x) { return 1-Math.Pow(Tanh(x),2); } public double ArcTan(double x) { return Math.Atan(x); } public double DArcTan(double x) { return 1/Math.Pow(x,2)+1; } //Rectified Linear Unit public double ReLU(double x) { return Math.Max(0,x);// x < 0 ? 0 : x; } public double DReLU(double x) { return Math.Max(0,1);// x < 0 ? 0 : x; } //Parameteric Rectified Linear Unit public double PReLU(double x, double a) { return x < 0 ? a*x : x; } public double DPReLU(double x, double a) { return x < 0 ? a : 1; } //Exponential Linear Unit public double ELU(double x, double a) { return x < 0 ? a*(Math.Pow(Math.E, x) - 1) : x; } public double DELU(double x, double a) { return x < 0 ? ELU(x, a)+a: 1; } public double SoftPlus(double x) { return Math.Log(Math.Exp(x)+1); } public double DSoftPlus(double x) { return Logistic(x); } public double BentIdentity(double x) { return (((Math.Sqrt(Math.Pow(x,2)+1))-1)/2)+x; } public double DBentIdentity(double x) { return (x/(2*Math.Sqrt(Math.Pow(x,2)+1)))+1; } // public float SoftExponential(float x) // { // // } public double Sinusoid(double x) { return Math.Sin(x); } public double DSinusoid(double x) { return Math.Cos(x); } public double Sinc(double x) { return x == 0 ? 1 : Math.Sin(x)/x; } public double DSinc(double x) { return x == 0 ? 0 : (Math.Cos(x)/x)-(Math.Sin(x)/Math.Pow(x,2)); } public double Gaussian(double x) { return Math.Pow(Math.E, Math.Pow(-x, 2)); } public double DGaussian(double x) { return -2*x*Math.Pow(Math.E, Math.Pow(-x,2)); } public double Bipolar(double x) { return x < 0 ? -1:1; } public double BipolarSigmoid(double x) { return (1-Math.Exp(-x))/(1+Math.Exp(-x)); } public double DBipolarSigmoid(double x) { return 0.5 * (1 + BipolarSigmoid(x)) * (1 - BipolarSigmoid(x)); } public double Scaler(double x, double min, double max) { return (x - min) / (max - min); } } public enum ActivationType { Identity, BinaryStep, Logistic, Tanh, ArcTan, ReLU, PReLU, ELU, SoftPlus, BentIdentity, Sinusoid, Sinc, Gaussian, Bipolar, BipolarSigmoid } 

不确定我的数学是否正确所以我不会将其作为答案发布。 如果有人愿意做错误检查我可以做出答案。