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

impl Copy for ActivationFunc

impl Clone for ActivationFunc

fn clone(&self) -> ActivationFunc

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for ActivationFunc

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

Formats the value using the given formatter.

impl Eq for ActivationFunc

impl PartialEq for ActivationFunc

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

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=.