Advanced LSTM Implementation with PyTorch

🚀 Overview

A sophisticated implementation of Long Short-Term Memory (LSTM) networks in PyTorch, featuring state-of-the-art architectural enhancements and optimizations. This implementation includes bidirectional processing capabilities and advanced regularization techniques, making it suitable for both research and production environments.

✨ Key Features

• Advanced Architecture

  • Bidirectional LSTM support for enhanced context understanding
  • Multi-layer stacking with proper gradient flow
  • Configurable hidden dimensions and layer depth
  • Efficient combined weight matrices implementation

• Training Optimizations

  • Layer Normalization for stable training
  • Orthogonal weight initialization
  • Optimized forget gate bias initialization
  • Dropout regularization between layers

• Production Ready

  • Clean, modular, and thoroughly documented code
  • Type hints for better IDE support
  • Factory pattern for easy model creation
  • Comprehensive testing suite

🏗️ Architecture

The implementation is structured in a modular hierarchy:

LSTMCell
   ↓
StackedLSTM
   ↓
LSTMNetwork

• LSTMCell: Core LSTM computation unit with layer normalization
• StackedLSTM: Manages multiple LSTM layers with proper interconnections
• LSTMNetwork: Top-level module with bidirectional support and output projection

💻 Installation

# Clone the repository
git clone https://github.com/yourusername/lstm-implementation.git

📊 Usage Example

# Configure your LSTM model
config = {
    'input_size': 3,
    'hidden_size': 64,
    'num_layers': 2,
    'output_size': 1,
    'dropout': 0.3,
    'bidirectional': True
}

# Create and use the model
model = create_lstm_model(config)
output = model(input_sequence)

🧪 Testing

The implementation includes a comprehensive testing suite:

# Run the full test suite
python lstm_test.py

The test suite includes:

• Synthetic sequence prediction tasks
• Training/validation split
• Performance visualization
• Prediction accuracy metrics

📈 Performance Visualization

The testing suite generates training curves and performance metrics:

# Generate performance plots
plot_training_history(train_losses, val_losses)

🔬 Technical Implementation Details

LSTM Cell Mathematics

The core LSTM cell implements the following equations:

f_t = σ(W_f · [h_{t-1}, x_t] + b_f)    # Forget gate
i_t = σ(W_i · [h_{t-1}, x_t] + b_i)    # Input gate
g_t = tanh(W_g · [h_{t-1}, x_t] + b_g)  # Candidate cell state
o_t = σ(W_o · [h_{t-1}, x_t] + b_o)    # Output gate

c_t = f_t ⊙ c_{t-1} + i_t ⊙ g_t       # New cell state
h_t = o_t ⊙ tanh(c_t)                  # New hidden state

Optimizations

• Layer Normalization: Applied to gate pre-activations and states
• Gradient Flow: Optimized through proper initialization and normalization
• Memory Efficiency: Combined weight matrices for faster computation

🛠️ Advanced Features

Bidirectional Processing

The implementation supports bidirectional LSTM processing:

• Forward pass processes sequence left-to-right
• Backward pass processes sequence right-to-left
• Outputs are concatenated for richer representations

Layer Normalization

Applied at multiple points for training stability:

• Gate pre-activations
• Cell states
• Hidden states

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🌟 Acknowledgments

Special thanks to:

• The PyTorch team for their excellent framework
• The deep learning community for their research and insights
• Understanding LSTMs
• Long Short-Term Memory (LSTM) - Hochreiter & Schmidhuber

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This implementation is part of my portfolio demonstrating advanced deep learning architectures and best practices in ML engineering.