Bayesian Neural Networks consider a distribution over the network's weights, which provides a tool to estimate the uncertainty of a neural network by sampling different models for each input. Variational Neural Networks (VNNs) consider a probability distribution over each layer's outputs and generate parameters for it with the corresponding sub-layers. We provide two Python implementations of VNNs with PyTorch and JAX machine learning libraries that ensure reproducibility of the experimental results and allow implementing uncertainty estimation methods easily in other projects.