Crate rust_lstm

Rust LSTM Library

A complete LSTM implementation with training capabilities, multiple optimizers, dropout regularization, and support for various architectures including peephole connections and bidirectional processing.

Core Components

• LSTM Cells: Standard and peephole LSTM implementations with full backpropagation
• Bidirectional LSTM: Process sequences in both directions with flexible output combination
• Networks: Multi-layer LSTM networks for sequence modeling
• Training: Complete training system with BPTT, gradient clipping, and validation
• Optimizers: SGD, Adam, and RMSprop optimizers with adaptive learning rates
• Loss Functions: MSE, MAE, and Cross-Entropy with numerically stable implementations
• Dropout: Input, recurrent, output dropout and zoneout regularization

Quick Start

use rust_lstm::models::lstm_network::LSTMNetwork;
use rust_lstm::training::create_basic_trainer;
 
// Create a 2-layer LSTM with 10 input features and 20 hidden units
let mut network = LSTMNetwork::new(10, 20, 2)
    .with_input_dropout(0.2, true)     // Variational input dropout
    .with_recurrent_dropout(0.3, true) // Variational recurrent dropout
    .with_output_dropout(0.1);         // Standard output dropout
 
let mut trainer = create_basic_trainer(network, 0.001);
 
// Train on your data
// trainer.train(&train_data, Some(&validation_data));

Re-exports

pub use models::lstm_network::LSTMNetwork;

pub use models::lstm_network::LayerDropoutConfig;

pub use models::gru_network::GRUNetwork;

pub use models::gru_network::LayerDropoutConfig as GRULayerDropoutConfig;

pub use models::gru_network::GRUNetworkCache;

pub use layers::lstm_cell::LSTMCell;

pub use layers::peephole_lstm_cell::PeepholeLSTMCell;

pub use layers::gru_cell::GRUCell;

pub use layers::gru_cell::GRUCellGradients;

pub use layers::gru_cell::GRUCellCache;

pub use layers::bilstm_network::BiLSTMNetwork;

pub use layers::bilstm_network::CombineMode;

pub use layers::bilstm_network::BiLSTMNetworkCache;

pub use layers::dropout::Dropout;

pub use layers::dropout::Zoneout;

pub use training::LSTMTrainer;

pub use training::ScheduledLSTMTrainer;

pub use training::TrainingConfig;

pub use training::create_basic_trainer;

pub use training::create_step_lr_trainer;

pub use training::create_one_cycle_trainer;

pub use training::create_cosine_annealing_trainer;

pub use optimizers::SGD;

pub use optimizers::Adam;

pub use optimizers::RMSprop;

pub use optimizers::ScheduledOptimizer;

pub use schedulers::LearningRateScheduler;

pub use schedulers::ConstantLR;

pub use schedulers::StepLR;

pub use schedulers::MultiStepLR;

pub use schedulers::ExponentialLR;

pub use schedulers::CosineAnnealingLR;

pub use schedulers::CosineAnnealingWarmRestarts;

pub use schedulers::OneCycleLR;

pub use schedulers::ReduceLROnPlateau;

pub use schedulers::LinearLR;

pub use schedulers::AnnealStrategy;

pub use schedulers::PolynomialLR;

pub use schedulers::CyclicalLR;

pub use schedulers::CyclicalMode;

pub use schedulers::ScaleMode;

pub use schedulers::WarmupScheduler;

pub use schedulers::LRScheduleVisualizer;

pub use loss::LossFunction;

pub use loss::MSELoss;

pub use loss::MAELoss;

pub use loss::CrossEntropyLoss;

pub use persistence::ModelPersistence;

pub use persistence::PersistentModel;

pub use persistence::ModelMetadata;

pub use persistence::PersistenceError;

Modules

layers

loss

models

optimizers

persistence

schedulers

training

utils

Main library module.