Grenade is a composable deep learning framework written in Haskell that allows concise, type-safe specifications of complex neural network architectures. It provides implementations for convolutional layers, pooling, LSTM cells, fully connected layers, and generative adversarial networks. The library leverages Haskell’s dependent typing to express network architectures and tensor shapes at the type level, ensuring compile-time correctness of layer dimensions.