Enum fann_sys::fann_activationfunc_enum

#[repr(C)]
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 = xs, d = 1s

• 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(-2sx))

• d = 2sy*(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(sx) = 2/(1 + exp(-2s*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(-xsx*s)

• d = -2xsys

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(-xsx*s)*2-1

• d = -2xs*(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 = ((xs) / 2) / (1 + |xs|) + 0.5

• d = s1/(2(1+|xs|)(1+|x*s|))

FANN_ELLIOTT_SYMMETRIC

Fast (symmetric sigmoid like) activation function defined by David Elliott

• span: -1 < y < 1

• y = (xs) / (1 + |xs|)

• d = s1/((1+|xs|)(1+|xs|))

FANN_LINEAR_PIECE

Bounded linear activation function.

• span: 0 <= y <= 1

• y = xs, d = 1s

FANN_LINEAR_PIECE_SYMMETRIC

Bounded linear activation function.

• span: -1 <= y <= 1

• y = xs, d = 1s

FANN_SIN_SYMMETRIC

Periodical sine activation function.

• span: -1 <= y <= 1

• y = sin(x*s)

• d = scos(xs)

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 = scos(xs)/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

impl Copy for fann_activationfunc_enum

impl Clone for fann_activationfunc_enum

fn clone(&self) -> fann_activationfunc_enum

Returns a copy of the value.

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

Performs copy-assignment from source.