Struct Rprop 

pub struct Rprop<T: Float + Send + Default> { /* private fields */ }

RPROP (Resilient Propagation) trainer An adaptive learning algorithm that only uses the sign of the gradient

Implementations

impl<T: Float + Send + Default> Rprop<T>

pub fn new() -> Self

pub fn with_parameters( self, increase_factor: T, decrease_factor: T, delta_min: T, delta_max: T, delta_zero: T, ) -> Self

pub fn with_error_function( self, error_function: Box<dyn ErrorFunction<T>>, ) -> Self

Trait Implementations

impl<T: Float + Send + Default> Default for Rprop<T>

fn default() -> Self

Returns the “default value” for a type.

impl<T: Float + Send + Default> TrainingAlgorithm<T> for Rprop<T>

fn train_epoch( &mut self, network: &mut Network<T>, data: &TrainingData<T>, ) -> Result<T, TrainingError>

Train for one epoch

fn calculate_error(&self, network: &Network<T>, data: &TrainingData<T>) -> T

Calculate the current error

fn count_bit_fails( &self, network: &Network<T>, data: &TrainingData<T>, bit_fail_limit: T, ) -> usize

Count bit fails

fn save_state(&self) -> TrainingState<T>

Save training state

fn restore_state(&mut self, state: TrainingState<T>)

Restore training state

fn set_callback(&mut self, callback: TrainingCallback<T>)

Set a callback function

fn call_callback( &mut self, epoch: usize, network: &Network<T>, data: &TrainingData<T>, ) -> bool

Call the callback if set