This repo contains a PyTorch implementation of the model outlined in Towards a Neural Statistician (Edwards & Storkey, ICLR 2017). All models are close approximations of those listed in the paper and should yield comparable results.
The code was written using Python 3.6, and currently works only with a GPU setup. Package requirements should be easy to handle using Conda and/or pip.
The four main experiments are each located in their own subdirectory. Each subdirectory contains the following five files:
data.py Methods for creating datasets, and custom providers leveraging PyTorch's Dataset class. nets.py Subnetworks used in Statistician model, implemented as subclasses of torch.nn.Module. These correspond to the shared encoder, statistic network, inference network, latent decoder, and observation decoder referred to in the paper.model.py Implementation of the Neural Statistician model.plot.py Utility functions for plotting results.run.py Driver program with tweakable command line args. The spatial, omniglot, and faces directories also contain an additional create.py file used for data preprocessing.
The following data sets are used:
Further preprocessing is needed. This is detailed below.
The following commands require you to be in the corresponding experiment directories. Checkpoints and figures are saved at intervals to a specified output directory. To save disk space, you can opt to save these only on the final epoch of training.
python synthrun.py --output-dir=outputpython spatialcreate.py --data-dir=mnist-datamnist-data must contain gzipped MNIST files. The above command will pickle two files to mnist-data/spatial.
python spatialrun.py --output-dir=output --data-dir=/mnist-data/spatialpython omnicreate.py --data-dir=omniglot-dataomniglot-data must contain the file chardata.mat. The above command will pickle a data file to the omniglot-data directory.
python omnirun.py --output-dir=output --data-dir=omniglot-data --mnist-data-dir=mnist-dataThe MNIST data directory needs to be specified here for testing of the model on unseen data.
python facescreate.py --data-dir=faces-datafaces-data must contain the original uncropped images, with an unaltered directory structure as given by the download. The above command will create another directory faces-data/64, with the same structure, but where images are cropped to 64 x 64. Note that this operation will take significant disk space (about 1GB).
python facesrun.py --output-dir=output --data-dir=faces/64Please use the issues tracker for questions, problems etc.
MIT License
Copyright (c) 2017-2018 Conor Durkan
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