TyXe: Pyro-based Bayesian neural nets for Pytorch

Abstract

We introduce TyXe, a Bayesian neural network library built on top of Pytorch and Pyro. Our leading design principle is to cleanly separate architecture, prior, inference and likelihood specification, allowing for a flexible workflow where users can quickly iterate over combinations of these components. In contrast to existing packages TyXe does not implement any layer classes, and instead relies on architectures defined in generic Pytorch code. TyXe then provides modular choices for canonical priors, variational guides, inference techniques, and layer selections for a Bayesian treatment of the specified architecture. Sampling tricks for variance reduction, such as local reparameterization or flipout, are implemented as effect handlers, which can be applied independently of other specifications. We showcase the ease of use of TyXe to explore Bayesian versions of popular models from various libraries: toy regression with a pure Pytorch neural network; large-scale image classification with torchvision ResNets; graph neural networks based on DGL; and Neural Radiance Fields built on top of Pytorch3D. Finally, we provide convenient abstractions for variational continual learning. In all cases the change from a deterministic to a Bayesian neural network comes with minimal modifications to existing code, offering a broad range of researchers and practitioners alike practical access to uncertainty estimation techniques. The library is available at https://github.com/TyXe-BDL/TyXe.

Consumption–Production Profile Categorization in Energy Communities

“…The accuracy and stability of these consumption and production categorization models may be improved with new non-linear shallow learning or deep learning method sequences. The non-linear shallow methods to be tested may include Random Forest or eXtreme Gradient Regressor, and deep learning methods such as a Hybrid Model of Convolutional Neural Networks and Recurrent Neural networks (Long Short-Term Memory Networks-LSTM-or Gated Recurrent Unit -GRU-), Bayesian neural networks or Transformers in Sequence like N-Beats [15,17,[26][27][28][29][30][31][32][33][34][35]. These deep learning techniques unveil non-linear relationships, and the scientific community is developing new hybrid models for forecasting photovoltaic power like the models created by Guanoluisa et al [36] using Bayesian optimization in the hyperparameters setting.…”