Implementation of Real-NVP (https://arxiv.org/abs/1605.08803) in Tensorflow. This was modified to work with datasets being used in the Online Structure Learning for SPNs paper.

Started with code from PixelCNN++ by OpenAI (https://github.com/openai/pixel-cnn)

Sample usage:

1. Install Python3.
2. Create directories for downloading dataset and saving checkpoints.
3. Seperate data into 10 .txt files and also create another file that keeps track of the mean and variance of each variable e.g. data/dataset_name/dataset_name.1.data
4. Run train.py. '--nr_gpu', which denotes the number of GPUs to use, should be specified.

Sample usage:

$ CUDA_VISIBLE_DEVICES=1,2 python3 train.py --nr_gpu=2 --data_dir=download --save_dir=checkpoints --load_params=0 --save_interval=2

Sample image from the model trained on CIFAR10. The test NLL was 3.51.