Merge pull request #3 from SyntaxSpirits/feature/complete-training-system

feat: Implement complete LSTM training system

Author: kholdrex

.gitignore

@@ -1,4 +1,28 @@
+# Rust build artifacts
 /target/
+**/*.rs.bk
+Cargo.lock
+
+# IDE files
 .vscode/
+.idea/
 
-Cargo.lock
+# OS generated files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Temporary files
+*~
+*.tmp
+*.swp
+*.swo
+
+# Coverage reports
+/target/debug/deps/*
+lcov.info
+*.profraw

CHANGELOG.md

@@ -0,0 +1,131 @@
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [0.2.0] - 2025-06-09
+
+### Added
+- **Complete Training System**: Full backpropagation through time (BPTT) implementation
+- **Multiple Optimizers**: SGD, Adam, and RMSprop with configurable parameters
+- **Loss Functions**: MSE, MAE, and Cross-Entropy with numerically stable implementations
+- **Training Infrastructure**:
+  - LSTMTrainer with configurable training loops
+  - Gradient clipping to prevent exploding gradients
+  - Validation support with metrics tracking
+  - Training configuration system
+- **Advanced Features**:
+  - Gradient accumulation across time steps
+  - Parameter state management for optimizers
+  - Comprehensive metrics tracking and history
+- **Enhanced Network Architecture**:
+  - Network-level backward propagation
+  - Multi-layer gradient computation
+  - Sequence processing with caching
+- **Examples and Documentation**:
+  - Complete training example with sine wave prediction
+  - Optimizer comparison demonstrations
+  - Comprehensive API documentation
+
+### Changed
+- **LSTM Cell**: Added backward propagation with full gradient computation
+- **LSTM Network**: Enhanced with training capabilities and caching
+- **Architecture**: Restructured for better separation of concerns
+- **Performance**: Optimized gradient computation and memory usage
+
+### Technical Details
+- Implemented mathematically correct BPTT algorithm
+- Added bias correction for Adam optimizer
+- Implemented numerically stable softmax function
+- Enhanced multi-layer gradient flow
+- Added proper parameter update mechanisms
+
+## [0.1.0] - 2024-06-10
+
+### Added
+- **Core LSTM Implementation**:
+  - Basic LSTM cell with forward propagation
+  - Standard LSTM equations implementation
+  - Multi-layer LSTM network support
+- **Peephole LSTM Variant**:
+  - Peephole connections for enhanced performance
+  - Direct cell state to gate connections
+- **Basic Architecture**:
+  - Modular design with separate layers and models
+  - Clean separation between cell and network levels
+  - Configurable network architecture (input size, hidden size, layers)
+- **Utility Functions**:
+  - Sigmoid and tanh activation functions
+  - Random weight initialization
+- **Examples**:
+  - Basic usage demonstrations
+  - Multi-layer network examples
+  - Time series prediction examples
+  - Text generation examples
+  - Peephole LSTM usage
+
+### Technical Details
+- Forward-pass only implementation
+- Xavier-uniform weight initialization
+- Support for variable sequence lengths
+- Proper hidden and cell state management
+
+## Architecture Evolution
+
+### Phase 1: Basic Implementation (v0.1.0)
+- Forward-pass only LSTM cells
+- Basic multi-layer networks
+- Simple examples and demonstrations
+
+### Phase 2: Complete Training Framework (v0.2.0)
+- Full backward propagation (BPTT)
+- Multiple optimization algorithms
+- Comprehensive loss functions
+- Training infrastructure with validation
+- Advanced gradient management
+
+### Phase 3: Documentation & Polish (Current)
+- Cleaned and optimized documentation
+- Enhanced code clarity
+- Comprehensive examples
+- Performance optimizations
+
+## Future Roadmap
+
+### Planned Features
+- **Enhanced Architectures**:
+  - Bidirectional LSTM
+  - GRU (Gated Recurrent Unit)
+  - Attention mechanisms
+- **Performance Optimizations**:
+  - GPU acceleration support
+  - Batch processing capabilities
+  - SIMD optimizations
+- **Advanced Training Features**:
+  - Learning rate scheduling
+  - Regularization techniques (dropout, weight decay)
+  - Early stopping
+- **Data Handling**:
+  - Built-in data preprocessing
+  - Common dataset loaders
+  - Data augmentation
+- **Visualization & Monitoring**:
+  - Training progress visualization
+  - Loss curve plotting
+  - Model analysis tools
+
+## Contributing
+
+When contributing to this project, please:
+1. Update the CHANGELOG.md file with your changes
+2. Follow the established coding style
+3. Add tests for new functionality
+4. Update documentation as needed
+
+## Version History Summary
+
+- **v0.1.0**: Initial LSTM implementation with forward pass
+- **v0.2.0**: Complete training system with BPTT and optimizers
+- **Current**: Documentation improvements and code polish 

Cargo.toml

@@ -1,13 +1,15 @@
 [package]
 name = "rust-lstm"
-version = "0.1.0"
+version = "0.2.0"
 authors = ["Alex Kholodniak <alexandrkholodniak@gmail.com>"]
 edition = "2021"
-description = "A Rust library for LSTM (Long Short-Term Memory) neural networks."
+description = "A complete LSTM neural network library with training capabilities, multiple optimizers, and peephole variants."
 license = "MIT"
 repository = "https://github.com/SyntaxSpirits/rust-lstm"
 homepage = "https://github.com/SyntaxSpirits/rust-lstm"
 documentation = "https://docs.rs/rust-lstm"
+keywords = ["lstm", "neural-network", "machine-learning", "deep-learning", "rnn"]
+categories = ["algorithms", "science"]
 
 [dependencies]
 ndarray = "0.15"

README.md

@@ -1,13 +1,17 @@
 # Rust-LSTM
 
-A simple LSTM (Long Short-Term Memory) neural network library implemented in Rust. This library provides basic functionalities to create and train LSTM networks.
+A comprehensive LSTM (Long Short-Term Memory) neural network library implemented in Rust. This library provides complete functionalities to create, train, and use LSTM networks for various sequence modeling tasks.
 
 ## Features
 
-- LSTM cell implementation
-- Multi-layer LSTM network
-- Random initialization of weights and biases
-- Forward pass through the network
+- **LSTM cell implementation** with forward and backward propagation
+- **Peephole LSTM variant** for enhanced performance
+- **Multi-layer LSTM networks** with configurable architecture
+- **Complete training system** with backpropagation through time (BPTT)
+- **Multiple optimizers**: SGD, Adam, RMSprop
+- **Loss functions**: MSE, MAE, Cross-entropy with softmax
+- **Training utilities**: gradient clipping, validation, metrics tracking
+- **Random initialization** of weights and biases
 
 ## Getting Started
 
@@ -32,7 +36,9 @@ cargo build
 
 ## Usage
 
-Here's a simple example demonstrating how to use the LSTM library:
+### Basic Forward Pass
+
+Here's a simple example demonstrating basic LSTM usage:
 
 ```rust
 use ndarray::Array2;
@@ -59,7 +65,86 @@ fn main() {
     // Print the output
     println!("Output: {:?}", output);
 }
+```
+
+### Training an LSTM Network
+
+Here's how to train an LSTM for time series prediction:
+
+```rust
+use ndarray::Array2;
+use rust_lstm::models::lstm_network::LSTMNetwork;
+use rust_lstm::training::{create_basic_trainer, TrainingConfig};
+use rust_lstm::optimizers::Adam;
+use rust_lstm::loss::MSELoss;
+
+fn main() {
+    // Create network
+    let network = LSTMNetwork::new(1, 10, 2);
+    
+    // Setup training with Adam optimizer
+    let loss_function = MSELoss;
+    let optimizer = Adam::new(0.001);
+    let mut trainer = LSTMTrainer::new(network, loss_function, optimizer);
+    
+    // Configure training
+    let config = TrainingConfig {
+        epochs: 100,
+        print_every: 10,
+        clip_gradient: Some(1.0),
+    };
+    trainer = trainer.with_config(config);
+    
+    // Generate some training data (sine wave prediction)
+    let train_data = generate_sine_wave_data();
+    
+    // Train the model
+    trainer.train(&train_data, None);
+    
+    // Make predictions
+    let predictions = trainer.predict(&input_sequence);
+}
+```
+
+### Advanced Features
+
+#### Using Different Optimizers
+
+```rust
+use rust_lstm::optimizers::{SGD, Adam, RMSprop};
+
+// SGD optimizer
+let sgd = SGD::new(0.01);
+
+// Adam optimizer with custom parameters
+let adam = Adam::with_params(0.001, 0.9, 0.999, 1e-8);
+
+// RMSprop optimizer
+let rmsprop = RMSprop::new(0.01);
+```
+
+#### Different Loss Functions
 
+```rust
+use rust_lstm::loss::{MSELoss, MAELoss, CrossEntropyLoss};
+
+// Mean Squared Error for regression
+let mse_loss = MSELoss;
+
+// Mean Absolute Error for robust regression
+let mae_loss = MAELoss;
+
+// Cross-Entropy for classification
+let ce_loss = CrossEntropyLoss;
+```
+
+#### Peephole LSTM
+
+```rust
+use rust_lstm::layers::peephole_lstm_cell::PeepholeLSTMCell;
+
+let cell = PeepholeLSTMCell::new(input_size, hidden_size);
+let (h_t, c_t) = cell.forward(&input, &h_prev, &c_prev);
 ```
 
 To run this example, save it as main.rs, and run:
@@ -68,6 +153,44 @@ To run this example, save it as main.rs, and run:
 cargo run
 ```
 
+## Examples
+
+The library includes several examples demonstrating different use cases:
+
+- `basic_usage.rs` - Simple forward pass example
+- `training_example.rs` - Complete training workflow with multiple optimizers
+- `time_series_prediction.rs` - Time series forecasting
+- `text_generation.rs` - Character-level text generation
+- `multi_layer_lstm.rs` - Multi-layer network usage
+- `peephole.rs` - Peephole LSTM variant
+
+Run examples with:
+
+```bash
+cargo run --example training_example
+cargo run --example time_series_prediction
+```
+
+## Architecture
+
+The library is organized into several modules:
+
+- **`layers`**: LSTM cell implementations (standard and peephole variants)
+- **`models`**: High-level network architectures
+- **`loss`**: Loss functions for training
+- **`optimizers`**: Optimization algorithms
+- **`training`**: Training utilities and trainer struct
+- **`utils`**: Common utility functions
+
+## Training Features
+
+- **Backpropagation Through Time (BPTT)**: Complete gradient computation for sequence modeling
+- **Gradient Clipping**: Prevents exploding gradients during training
+- **Multiple Optimizers**: SGD, Adam, RMSprop with configurable parameters
+- **Validation Support**: Track validation metrics during training
+- **Metrics Tracking**: Loss curves and training progress monitoring
+- **Flexible Training Loop**: Configurable epochs, learning rates, and logging
+
 ## Running the Tests
 
 To run the tests included with Rust-LSTM, execute:

examples/peephole.rs

@@ -0,0 +1,23 @@
+use ndarray::{arr2, Array2};
+use rust_lstm::layers::peephole_lstm_cell::PeepholeLSTMCell;
+
+fn main() {
+    let input_size = 3;
+    let hidden_size = 2;
+
+    // Create Peephole cell
+    let cell = PeepholeLSTMCell::new(input_size, hidden_size);
+
+    // Single input vector: shape (3,1)
+    let input: Array2<f64> = arr2(&[[0.5], [0.1], [-0.3]]);
+
+    // Initialize hidden, cell
+    let h_prev = Array2::<f64>::zeros((hidden_size, 1));
+    let c_prev = Array2::<f64>::zeros((hidden_size, 1));
+
+    // Single timestep forward pass
+    let (h_t, c_t) = cell.forward(&input, &h_prev, &c_prev);
+
+    println!("h_t = \n{:?}", h_t);
+    println!("c_t = \n{:?}", c_t);
+}