Struct LSTMTrainer

pub struct LSTMTrainer<L: LossFunction, O: Optimizer> {
    pub network: LSTMNetwork,
    pub loss_function: L,
    pub optimizer: O,
    pub config: TrainingConfig,
    pub metrics_history: Vec<TrainingMetrics>,
}

Main trainer for LSTM networks with configurable loss and optimizer

Fields

network: LSTMNetwork

loss_function: L

optimizer: O

config: TrainingConfig

metrics_history: Vec<TrainingMetrics>

Implementations

impl<L: LossFunction, O: Optimizer> LSTMTrainer<L, O>

pub fn new(network: LSTMNetwork, loss_function: L, optimizer: O) -> Self

pub fn with_config(self, config: TrainingConfig) -> Self

pub fn train_sequence( &mut self, inputs: &[Array2<f64>], targets: &[Array2<f64>], ) -> f64

Train on a single sequence using backpropagation through time (BPTT)

pub fn train( &mut self, train_data: &[(Vec<Array2<f64>>, Vec<Array2<f64>>)], validation_data: Option<&[(Vec<Array2<f64>>, Vec<Array2<f64>>)]>, )

Train for multiple epochs with optional validation

pub fn evaluate(&mut self, data: &[(Vec<Array2<f64>>, Vec<Array2<f64>>)]) -> f64

Evaluate model performance on validation data

pub fn predict(&mut self, inputs: &[Array2<f64>]) -> Vec<Array2<f64>>

Generate predictions for input sequences

pub fn get_latest_metrics(&self) -> Option<&TrainingMetrics>

pub fn get_metrics_history(&self) -> &[TrainingMetrics]

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

Set network to training mode