Struct Hextral 

pub struct Hextral { /* private fields */ }

Implementations

impl Hextral

pub fn new( input_size: usize, hidden_sizes: &[usize], output_size: usize, activation: ActivationFunction, optimizer: Optimizer, ) -> Self

pub fn set_regularization(&mut self, reg: Regularization)

Set regularization

pub fn set_loss_function(&mut self, loss: LossFunction)

Set loss function

pub fn enable_batch_norm(&mut self)

Enable batch normalization for all hidden layers

pub fn disable_batch_norm(&mut self)

Disable batch normalization

pub fn set_training_mode(&mut self, training: bool)

Set training mode for batch normalization

pub async fn forward(&self, input: &DVector<f64>) -> DVector<f64>

pub async fn predict(&self, input: &DVector<f64>) -> DVector<f64>

pub async fn predict_batch(&self, inputs: &[DVector<f64>]) -> Vec<DVector<f64>>

pub fn compute_loss( &self, prediction: &DVector<f64>, target: &DVector<f64>, ) -> f64

Compute loss between prediction and target

pub fn compute_loss_gradient( &self, prediction: &DVector<f64>, target: &DVector<f64>, ) -> DVector<f64>

Compute loss gradient for backpropagation

pub async fn train_step( &mut self, input: &DVector<f64>, target: &DVector<f64>, learning_rate: f64, ) -> f64

pub async fn train( &mut self, train_inputs: &[DVector<f64>], train_targets: &[DVector<f64>], learning_rate: f64, epochs: usize, batch_size: Option<usize>, val_inputs: Option<&[DVector<f64>]>, val_targets: Option<&[DVector<f64>]>, early_stopping: Option<EarlyStopping>, checkpoint_config: Option<CheckpointConfig>, ) -> Result<(Vec<f64>, Vec<f64>), Box<dyn Error>>

Full async training method with early stopping and checkpoints

pub async fn evaluate( &self, test_inputs: &[DVector<f64>], test_targets: &[DVector<f64>], ) -> f64

pub fn parameter_count(&self) -> usize

Get the number of parameters in the network

pub fn architecture(&self) -> Vec<usize>

Get network architecture info

pub fn get_weights(&self) -> Vec<(DMatrix<f64>, DVector<f64>)>

Save network weights (simplified serialization)

pub fn set_weights(&mut self, weights: Vec<(DMatrix<f64>, DVector<f64>)>)

Load network weights (simplified deserialization)

Trait Implementations

impl Clone for Hextral

fn clone(&self) -> Hextral

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<'de> Deserialize<'de> for Hextral

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for Hextral

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.