HuwCampbell/grenade

Health v2 · maintenance only

Adoption not part of the score

1449 stars · 81 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Grenade is a composable, dependently typed deep learning library for Haskell that lets users specify neural network architectures at the type level, including convolutional, fully connected, and recurrent layers like LSTM. Networks are defined as heterogeneous lists of layers with shape-checked data flow, requiring minimal term-level code.

Use cases

• train a CNN for MNIST digit classification in Haskell
• define an LSTM recurrent network for character-level text generation
• specify a neural network architecture with compile-time shape checking
• experiment with generative adversarial networks
• build deep learning models without leaving the Haskell ecosystem

When to choose

• you are a Haskell developer wanting type-safe neural network definitions
• you want compile-time verification of layer shapes and data dimensions
• you need a concise declarative way to specify CNNs or RNNs

When to avoid

• you need GPU acceleration or production-scale training performance
• you want a large ecosystem of pretrained models and tooling
• you are not working in Haskell

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning cross-platform neural-networks cnn rnn lstm gan dependently-typed haskell

Member repositories

For agents

markdown · JSON · MCP: product_card(name="HuwCampbell/grenade")