Enum fann_sys::fann_activationfunc_enum

pub enum fann_activationfunc_enum {
    FANN_NONE,
    FANN_LINEAR,
    FANN_THRESHOLD,
    FANN_THRESHOLD_SYMMETRIC,
    FANN_SIGMOID,
    FANN_SIGMOID_STEPWISE,
    FANN_SIGMOID_SYMMETRIC,
    FANN_SIGMOID_SYMMETRIC_STEPWISE,
    FANN_GAUSSIAN,
    FANN_GAUSSIAN_SYMMETRIC,
    FANN_GAUSSIAN_STEPWISE,
    FANN_ELLIOTT,
    FANN_ELLIOTT_SYMMETRIC,
    FANN_LINEAR_PIECE,
    FANN_LINEAR_PIECE_SYMMETRIC,
    FANN_SIN_SYMMETRIC,
    FANN_COS_SYMMETRIC,
    FANN_SIN,
    FANN_COS,
}

The activation functions used for the neurons during training. The activation functions can either be defined for a group of neurons by fann_set_activation_function_hidden and fann_set_activation_function_output, or it can be defined for a single neuron by fann_set_activation_function.

The steepness of an activation function is defined in the same way by fann_set_activation_steepness_hidden, fann_set_activation_steepness_output and fann_set_activation_steepness.

The functions are described with functions where:

• x is the input to the activation function,

• y is the output,

• s is the steepness and

• d is the derivation.

Variants

FANN_NONE	Neuron does not exist or does not have an activation function.
FANN_LINEAR	Linear activation function. span: -inf < y < inf y = x*s, d = 1*s Can NOT be used in fixed point.
FANN_THRESHOLD	Threshold activation function. x < 0 -> y = 0, x >= 0 -> y = 1 Can NOT be used during training.
FANN_THRESHOLD_SYMMETRIC	Threshold activation function. x < 0 -> y = 0, x >= 0 -> y = 1 Can NOT be used during training.
FANN_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)
FANN_SIGMOID_STEPWISE	Stepwise linear approximation to sigmoid. Faster than sigmoid but a bit less precise.
FANN_SIGMOID_SYMMETRIC	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))
FANN_SIGMOID_SYMMETRIC_STEPWISE	Stepwise linear approximation to symmetric sigmoid. Faster than symmetric sigmoid but a bit less precise.
FANN_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
FANN_GAUSSIAN_SYMMETRIC	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
FANN_GAUSSIAN_STEPWISE	Stepwise linear approximation to gaussian. Faster than gaussian but a bit less precise. NOT implemented yet.
FANN_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|))
FANN_ELLIOTT_SYMMETRIC	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|))
FANN_LINEAR_PIECE	Bounded linear activation function. span: 0 <= y <= 1 y = x*s, d = 1*s
FANN_LINEAR_PIECE_SYMMETRIC	Bounded linear activation function. span: -1 <= y <= 1 y = x*s, d = 1*s
FANN_SIN_SYMMETRIC	Periodical sine activation function. span: -1 <= y <= 1 y = sin(x*s) d = s*cos(x*s)
FANN_COS_SYMMETRIC	Periodical cosine activation function. span: -1 <= y <= 1 y = cos(x*s) d = s*-sin(x*s)
FANN_SIN	Periodical sine activation function. span: 0 <= y <= 1 y = sin(x*s)/2+0.5 d = s*cos(x*s)/2
FANN_COS	Periodical cosine activation function. span: 0 <= y <= 1 y = cos(x*s)/2+0.5 d = s*-sin(x*s)/2

Trait Implementations

Derived Implementations

impl Clone for fann_activationfunc_enum

fn clone(&self) -> fann_activationfunc_enum

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

impl Copy for fann_activationfunc_enum