pyautoencoder 1.0.3

A Python package offering implementations of state-of-the-art autoencoder architectures in PyTorch.

pyautoencoder is a lightweight Python package offering clean, minimal implementations of foundational autoencoder architectures in PyTorch. It is designed for researchers, educators, and practitioners seeking a reliable base for experimentation, extension, or instruction.

📦 Installation

pip install pyautoencoder

Or install from source:

git clone https://github.com/andrea-pollastro/pyautoencoder.git
cd pyautoencoder
pip install -e .

🚀 Quick Example

import torch
from pyautoencoder.models import Autoencoder

# Define encoder and decoder
encoder = torch.nn.Sequential(
    torch.nn.Flatten(),
    torch.nn.Linear(784, 32)
)

decoder = torch.nn.Sequential(
    torch.nn.Linear(32, 784),
    torch.nn.Unflatten(1, (1, 28, 28))
)

# Initialize model
model = Autoencoder(encoder, decoder)

# Forward pass
x = torch.randn(64, 1, 28, 28)
x_hat, z = model(x)

🗺️ Roadmap

• Autoencoder (AE)
• Variational Autoencoder (VAE)
• Hierarchical VAE (HVAE)
• Importance-Weighted AE (IWAE)
• Denoising Autoencoder (DAE)
• Sparse Autoencoder (SAE)

🤝 Contributing

Contributions are welcome — especially new autoencoder variants, training examples, and documentation improvements. Please open an issue or pull request to discuss any changes.

📝 Citing

@misc{pollastro2025pyautoencoder,
  Author = {Andrea Pollastro},
  Title = {pyautoencoder},
  Year = {2025},
  Publisher = {GitHub},
  Journal = {GitHub repository},
  Howpublished = {\url{https://github.com/andrea-pollastro/pyautoencoder}}
}

📄 License

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