This is the code used in the experiments described in the blog post Quantifying Uncertainty in Neural Networks.

To run the code, first download the CIFAR-10 and CIFAR-100 data sets. Extract them, and put them in a directory data as data/cifar-10-batches-py and data/cifar-100-python.

Configure the parameters in train.py (or leave them as the default) and create a directory nets for the learned weights to be written to. Run python inference.py to generate a directory creating the misclassifications from CIFAR-100. Finally, run python train.py to plot the image grids.

Those interested in further reading, should visit:

1. What My Deep Model Doesn't Know...

2. Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference