Activation
计算激活函数值，并将其写入传递的向量/矩阵。
bool vector::Activation(
vector& vect_out, // 获取函数值的向量
ENUM_ACTIVATION_FUNCTION activation, // 激活函数
... // 附加参数
);
bool matrix::Activation(
matrix& matrix_out, // 获取函数值的矩阵
ENUM_ACTIVATION_FUNCTION activation // 激活函数
);
bool matrix::Activation(
matrix& matrix_out, // 获取函数值的矩阵
ENUM_ACTIVATION_FUNCTION activation, // 激活函数
ENUM_MATRIX_AXIS axis, // 轴线
... // 附加参数
);参数
vect_out/matrix_out
[out] 获取激活函数计算值的向量或矩阵。
activation
[in] ENUM_ACTIVATION_FUNCTION枚举中的激活函数。
axis
[in] ENUM_MATRIX_AXIS枚举值（AXIS_HORZ — 横轴，AXIS_VERT — 竖轴）。
...
[in] 某些激活函数所需的附加参数。如果没有指定参数，则使用默认值。
返回值
如果成功返回true，否则返回false。
附加参数
某些激活函数接受附加参数。如果没有指定参数，则使用默认值
AF_ELU (Exponential Linear Unit)
double alpha=1.0
Activation function: if(x>=0) f(x) = x
else f(x) = alpha * (exp(x)-1)
AF_LINEAR
double alpha=1.0
double beta=0.0
Activation function: f(x) = alpha*x + beta
AF_LRELU (Leaky REctified Linear Unit)
double alpha=0.3
Activation function: if(x>=0) f(x)=x
else f(x) = alpha*x
AF_RELU (REctified Linear Unit)
double alpha=0.0
double max_value=0.0
double treshold=0.0
Activation function: if(alpha==0) f(x) = max(x,0)
else if(x>max_value) f(x) = x
else f(x) = alpha*(x - treshold)
AF_SWISH
double beta=1.0
Activation function: f(x) = x / (1+exp(-x*beta))
AF_TRELU (Thresholded REctified Linear Unit)
double theta=1.0
Activation function: if(x>theta) f(x) = x
else f(x) = 0
AF_PRELU (Parametric REctified Linear Unit)
double alpha[] - learned array of coeefficients
Activation function: if(x[i]>=0) f(x)[i] = x[i]
else f(x)[i] = alpha[i] * x[i]注意
在人工神经网络中，神经元的激活函数决定输出信号，输出信号由一个输入信号或一组输入信号定义。激活函数的选择对神经网络的性能有很大的影响。不同的模型部件(层)可以使用不同的激活函数。
使用附加参数的示例：
vector x={0.1, 0.4, 0.9, 2.0, -5.0, 0.0, -0.1};
vector y;
x.Activation(y,AF_ELU);
Print(y);
x.Activation(y,AF_ELU,2.0);
Print(y);
Print("");
x.Activation(y,AF_LINEAR);
Print(y);
x.Activation(y,AF_LINEAR,2.0);
Print(y);
x.Activation(y,AF_LINEAR,2.0,5.0);
Print(y);
Print("");
x.Activation(y,AF_LRELU);
Print(y);
x.Activation(y,AF_LRELU,1.0);
Print(y);
x.Activation(y,AF_LRELU,0.1);
Print(y);
Print("");
x.Activation(y,AF_RELU);
Print(y);
x.Activation(y,AF_RELU,2.0,0.5);
Print(y);
x.Activation(y,AF_RELU,2.0,0.5,1.0);
Print(y);
Print("");
x.Activation(y,AF_SWISH);
Print(y);
x.Activation(y,AF_SWISH,2.0);
Print(y);
Print("");
x.Activation(y,AF_TRELU);
Print(y);
x.Activation(y,AF_TRELU,0.3);
Print(y);
Print("");
vector a=vector::Full(x.Size(),2.0);
x.Activation(y,AF_PRELU,a);
Print(y);
/* Results
[0.1,0.4,0.9,2,-0.993262053000915,0,-0.095162581964040]
[0.1,0.4,0.9,2,-1.986524106001829,0,-0.190325163928081]
[0.1,0.4,0.9,2,-5,0,-0.1]
[0.2,0.8,1.8,4,-10,0,-0.2]
[5.2,5.8,6.8,9,-5,5,4.8]
[0.1,0.4,0.9,2,-1.5,0,-0.03]
[0.1,0.4,0.9,2,-5,0,-0.1]
[0.1,0.4,0.9,2,-0.5,0,-0.01]
[0.1,0.4,0.9,2,0,0,0]
[0.2,0.8,0.9,2,-10,0,-0.2]
[-1.8,-1.2,0.9,2,-12,-2,-2.2]
[0.052497918747894,0.239475064044981,0.6398545523625035,1.761594155955765,-0.03346425462142428,0,-0.047502081252106]
[0.054983399731247,0.275989792451045,0.7723340415895611,1.964027580075817,-0.00022698934351217,0,-0.045016600268752]
[0,0,0,2,0,0,0]
[0,0.4,0.9,2,0,0,0]
[0.1,0.4,0.9,2,-10,0,-0.2]
*/