shobrook/sequitur

It implements three different autoencoder architectures in PyTorch, and a predefined training loop. Each autoencoder learns to represent input sequences as lower-dimensional, fixed-size vectors. This can be useful for finding patterns among sequences, clustering sequences, or converting sequences into inputs for other algorithms. First, you need to prepare a set of example sequences to train an autoencoder on. This training set should be a list of torch.Tensors, where each tensor has shape [num_elements, *num_features].