This repository contains code for hierarchical convolutional variational autoencoders with Hamiltonian Monte Carlo, which accompanies my MPhil thesis at University of Cambridge.
The reposiroty contains 7 Python files:
models/hmc.py # Basic version of Hamiltonian Monte Carlo
models/blocks.py # Conv/ConvTranspose building blocks for the model
models/vae.py # VAE with hierarchical latent variables
models/hmc_vae.py # VAE with hierarchical latent variables and HMC
image_dataset.py # Download and preprocess image datasets
utils.py # Utility for turning on/off parts of a model
train.py # Main entry point for traning
And my thesis thesis.pdf that details theoretical background, implementation details, and experimental results.
The command below trains a model with 2 latent layers each containing 30 units, and 64 filters, on the dataset CIFAR10 (which is stored in ./data), for 100 epochs (100 variational epochs and hard-coded 3 HMC epochs), with the test set evaluated after every 1 epoch, where both trained model and test set scores are written to /path/to/save/.
python3 train.py --data_name CIFAR10 --data_root ./data \
--hidden_channels 64 --epochs 100 --eval_interval 1 \
--savedir /path/to/save/ --latent_dims 30 30
Encoder
Decoder
@Misc{HMC-VAE,
author = {Haoran Peng},
title = {Outlier Detection with Hierarchical VAEs and Hamiltonian Monte Carlo},
year = {2022},
url = "https://github.com/GavinPHR/HMC-VAE"
}
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