Enum fann::ActivationFunc

pub enum ActivationFunc {
    Linear,
    Threshold,
    ThresholdSymmetric,
    Sigmoid,
    SigmoidStepwise,
    SigmoidSymmetric,
    SigmoidSymmetricStepwise,
    Gaussian,
    GaussianSymmetric,
    GaussianStepwise,
    Elliott,
    ElliottSymmetric,
    LinearPiece,
    LinearPieceSymmetric,
    SinSymmetric,
    CosSymmetric,
    Sin,
    Cos,
}

The activation functions used for the neurons during training. They can either be set for a group of neurons using set_activation_func_hidden and set_activation_func_output, or for a single neuron using set_activation_func.

Similarly, the steepness of an activation function is specified using set_activation_steepness_hidden, set_activation_steepness_output and set_activation_steepness.

In the descriptions of the functions:

• x is the input to the activation function,

• y is the output,

• s is the steepness and

• d is the derivation.

Variants

Linear	Linear activation function. span: -inf < y < inf y = x*s, d = 1*s Can NOT be used in fixed point.
Threshold	Threshold activation function. x < 0 -> y = 0, x >= 0 -> y = 1 Can NOT be used during training.
ThresholdSymmetric	Threshold activation function. x < 0 -> y = 0, x >= 0 -> y = 1 Can NOT be used during training.
Sigmoid	Sigmoid activation function. One of the most used activation functions. span: 0 < y < 1 y = 1/(1 + exp(-2*s*x)) d = 2*s*y*(1 - y)
SigmoidStepwise	Stepwise linear approximation to sigmoid. Faster than sigmoid but a bit less precise.
SigmoidSymmetric	Symmetric sigmoid activation function, aka. tanh. One of the most used activation functions. span: -1 < y < 1 y = tanh(s*x) = 2/(1 + exp(-2*s*x)) - 1 d = s*(1-(y*y))
SigmoidSymmetricStepwise	Stepwise linear approximation to symmetric sigmoid. Faster than symmetric sigmoid but a bit less precise.
Gaussian	Gaussian activation function. 0 when x = -inf, 1 when x = 0 and 0 when x = inf span: 0 < y < 1 y = exp(-x*s*x*s) d = -2*x*s*y*s
GaussianSymmetric	Symmetric gaussian activation function. -1 when x = -inf, 1 when x = 0 and 0 when x = inf span: -1 < y < 1 y = exp(-x*s*x*s)*2-1 d = -2*x*s*(y+1)*s
GaussianStepwise	Stepwise linear approximation to gaussian. Faster than gaussian but a bit less precise. NOT implemented yet.
Elliott	Fast (sigmoid like) activation function defined by David Elliott span: 0 < y < 1 y = ((x*s) / 2) / (1 + |x*s|) + 0.5 d = s*1/(2*(1+|x*s|)*(1+|x*s|))
ElliottSymmetric	Fast (symmetric sigmoid like) activation function defined by David Elliott span: -1 < y < 1 y = (x*s) / (1 + |x*s|) d = s*1/((1+|x*s|)*(1+|x*s|))
LinearPiece	Bounded linear activation function. span: 0 <= y <= 1 y = x*s, d = 1*s
LinearPieceSymmetric	Bounded linear activation function. span: -1 <= y <= 1 y = x*s, d = 1*s
SinSymmetric	Periodical sine activation function. span: -1 <= y <= 1 y = sin(x*s) d = s*cos(x*s)
CosSymmetric	Periodical cosine activation function. span: -1 <= y <= 1 y = cos(x*s) d = s*-sin(x*s)
Sin	Periodical sine activation function. span: 0 <= y <= 1 y = sin(x*s)/2+0.5 d = s*cos(x*s)/2
Cos	Periodical cosine activation function. span: 0 <= y <= 1 y = cos(x*s)/2+0.5 d = s*-sin(x*s)/2

Methods

impl ActivationFunc

fn from_fann_activationfunc_enum(af_enum: fann_activationfunc_enum) -> FannResult<ActivationFunc>

Create an ActivationFunc from a fann_sys::fann_activationfunc_enum.

fn to_fann_activationfunc_enum(&self) -> fann_activationfunc_enum

Return the fann_sys::fann_activationfunc_enum corresponding to this ActivationFunc.

Trait Implementations

Derived Implementations

impl PartialEq for ActivationFunc

fn eq(&self, __arg_0: &ActivationFunc) -> bool

fn ne(&self, __arg_0: &ActivationFunc) -> bool

impl Eq for ActivationFunc

impl Debug for ActivationFunc

fn fmt(&self, __arg_0: &mut Formatter) -> Result

impl Clone for ActivationFunc

fn clone(&self) -> ActivationFunc

fn clone_from(&mut self, source: &Self)

impl Copy for ActivationFunc