Struct GRUNetwork 

pub struct GRUNetwork {
    pub input_size: usize,
    pub hidden_size: usize,
    pub num_layers: usize,
    pub is_training: bool,
    /* private fields */
}

Multi-layer GRU network for sequence modeling

Fields

input_size: usize

hidden_size: usize

num_layers: usize

is_training: bool

Implementations

impl GRUNetwork

pub fn new(input_size: usize, hidden_size: usize, num_layers: usize) -> Self

Creates a new multi-layer GRU network

pub fn with_input_dropout(self, dropout_rate: f64, variational: bool) -> Self

Apply uniform dropout across all layers

pub fn with_recurrent_dropout( self, dropout_rate: f64, variational: bool, ) -> Self

pub fn with_output_dropout(self, dropout_rate: f64) -> Self

pub fn with_layer_dropout(self, configs: Vec<LayerDropoutConfig>) -> Self

Apply layer-specific dropout configuration

pub fn train(&mut self)

pub fn eval(&mut self)

pub fn forward( &mut self, input: &Array2<f64>, hx: &[Array2<f64>], ) -> Vec<Array2<f64>>

Forward pass for a single time step

pub fn forward_sequence_with_cache( &mut self, sequence: &[Array2<f64>], ) -> (Vec<(Array2<f64>, Vec<Array2<f64>>)>, Vec<GRUNetworkCache>)

Forward pass for a sequence with caching for training

pub fn backward( &self, dhy: &Array2<f64>, cache: &GRUNetworkCache, ) -> (Vec<GRUCellGradients>, Array2<f64>)

Backward pass for training

pub fn update_parameters<O: Optimizer>( &mut self, gradients: &[GRUCellGradients], optimizer: &mut O, )

Update parameters using optimizer

pub fn zero_gradients(&self) -> Vec<GRUCellGradients>

Initialize zero gradients for all layers

pub fn get_cells(&self) -> &[GRUCell]

Get references to cells for inspection

pub fn get_cells_mut(&mut self) -> &mut [GRUCell]

Get mutable references to cells

Trait Implementations

impl Clone for GRUNetwork

fn clone(&self) -> GRUNetwork

Returns a duplicate of the value.

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

Performs copy-assignment from source.