This project aims to reproduce the results of Blundell et al. in their paper "Weight Uncertainty in Neural Networks" from 2015. This paper presents Bayesian neural networks that only require a minor modification to the backpropagation algorithm, which they call "Bayes by Backprop" (BBB). We also perform a number of extensions further to the original publication.

You find the performed experiments in individual folders, each containing code and results. We performed the following experiments:

• Image classification through fully-connected networks (FCN) in FCN_Image_Classification/,
• Regression on a toy example in Regression/,
• Reinforcement learning in contextual bandits in Contextual Bandit/,
• (Extension) Pokémon type classification from primary colour in Pokemon/,
• (Extension) A PyTorch BBB framework to apply BBB to any network architecture, studied for CNNs and DensetNets, in BayesCNN/.

The most notable contribution is our BBB PyTorch framework. As far we are concerned, there has thus far not existed a comparably versatile implementation of BBB.

There are two main scripts in the FCN_Image_Classification/ folder, SGD.py and BBB.py. The latter runs image classification through BBB, whereas SGD.py for regular FCNs, with or without dropout.

The BBB experiments are called as follows:

python3 BBB.py [hidden units per layer] [mnist|fmnist|cifar10]

The SGD experiments are executed in the same fashion:

python3 SGD.py [hidden units per layer] [mnist|fmnist|cifar10] [mlp|dropout]

Both scripts run for 600 epochs, then save a report and the generated model in Results/.

We also provide a script for weight pruning, that is run on the resulting model as follows:

python3 WeightPruning.py [hidden units per layer] [path to model]

Again, this script stores its results in Results/. The results consist of pruned models and plots, as in the original paper. The models can then be further tested for their classification errors. For the case of BBB, you can just uncomment the last few lines of BBB.py script.

The script Regression.py inside the Regression/ directory, executes both bayesian and standard learning on the specified non-linear curve. Its results are stored in Results/ directory while the generated model is stored as Regression.pth.

The experiment is executed in a Jupyter Notebook file, bandit_gui.ipynb, which executes a run of 50,000 steps for the agents and stores the obtained cumulative regrets in a .csv file in the Results/ directory.

Inside the Pokemon/ directory, the script Pokemon.py serves two tasks: generation of the training samples by amalgamating the different sources and using this data to train the model. Visualization.py helps to visualize the prediction across the colour pallete while UncertaintyVisual.py helps to capture model uncertainity.

In the BayesCNN directory, the bayes.py file is a generic BBB wrapper that can transform any network architecture into a bayesian network. The notebook CIFAR10.ipynb gives a workflow for building a frequentist/bayesian network, train it on CIFAR10 and record evolution of validation accuracies.

• C. Blundell, J. Cornebise, K. Kavukcuoglu and D. Wierstra, ‘Weight uncertainty in neural networks’, in Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, ser. ICML’15, 2015, pp. 1613–1622.