BRUNO: A Deep Recurrent Model for Exchangeable Data

This is an official code for reproducing the main results from our NIPS'18 paper:

I. Korshunova, J. Degrave, F. Huszár, Y. Gal, A. Gretton, J. Dambre
BRUNO: A Deep Recurrent Model for Exchangeable Data
arxiv.org/abs/1802.07535

and from our NIPS'18 Bayesian Deep Learning workshop paper:

I. Korshunova, Y. Gal, J. Dambre, A. Gretton
Conditional BRUNO: A Deep Recurrent Process for Exchangeable Labelled Data bayesiandeeplearning.org/2018/papers/40.pdf

Requirements

The code was used with the following settings:

• python3
• tensorflow-gpu==1.7.0
• scikit-image==0.13.1
• numpy==1.14.2
• scipy==1.0.0

Datasets

Below we list files for every dataset that should be stored in a data/ directory inside a project folder.

MNIST

Download from yann.lecun.com/exdb/mnist/

 data/train-images-idx3-ubyte.gz
 data/train-labels-idx1-ubyte.gz
 data/t10k-images-idx3-ubyte.gz
 data/t10k-labels-idx1-ubyte.gz

Fashion MNIST

Download from github.com/zalandoresearch/fashion-mnist

data/fashion_mnist/train-images-idx3-ubyte.gz
data/fashion_mnist/train-labels-idx1-ubyte.gz
data/fashion_mnist/t10k-images-idx3-ubyte.gz
data/fashion_mnist/t10k-labels-idx1-ubyte.gz

Omniglot

Download and unzip files from github.com/brendenlake/omniglot/tree/master/python

data/images_background
data/images_evaluation

Download .pkl files from github.com/renmengye/few-shot-ssl-public#omniglot. These are used to make train-test-validation split.

data/train_vinyals_aug90.pkl
data/test_vinyals_aug90.pkl
data/val_vinyals_aug90.pkl

Run utils.py to preprocess Omniglot images

data/omniglot_x_train.npy
data/omniglot_y_train.npy
data/omniglot_x_test.npy
data/omniglot_y_test.npy
data/omniglot_valid_classes.npy

CIFAR-10

This dataset will be downloaded directly with the first call to CIFAR-10 models.

data/cifar/cifar-10-batches-py

Training and testing

There are configuration files in config_rnn for every model we used in the paper and a bunch of testing scripts. Below are examples on how to train and test Omniglot models.

Training (supports multiple gpus)

CUDA_VISIBLE_DEVICES=0,1 python3 -m config_rnn.train  --config_name bn2_omniglot_tp --nr_gpu 2

Fine-tuning (to be used on one gpu only)

CUDA_VISIBLE_DEVICES=0 python3 -m config_rnn.train_finetune  --config_name bn2_omniglot_tp_ft_1s_20w

Generating samples

CUDA_VISIBLE_DEVICES=0 python3 -m config_rnn.test_samples  --config_name bn2_omniglot_tp_ft_1s_20w

Few-shot classification

CUDA_VISIBLE_DEVICES=0 python3 -m config_rnn.test_few_shot_omniglot  --config_name bn2_omniglot_tp --seq_len 2 --batch_size 20
CUDA_VISIBLE_DEVICES=0 python3 -m config_rnn.test_few_shot_omniglot  --config_name bn2_omniglot_tp_ft_1s_20w --seq_len 2 --batch_size 20

Here, batch_size = k and seq_len = n + 1 to test the model in a k-way, n-shot setting.

Citation

Please cite our paper when using this code for your research. If you have any questions, please send me an email at [email protected]

@incollection{bruno2018,
    title = {BRUNO: A Deep Recurrent Model for Exchangeable Data},
    author = {Korshunova, Iryna and Degrave, Jonas and Huszar, Ferenc and Gal, Yarin and Gretton, Arthur and Dambre, Joni},
    booktitle = {Advances in Neural Information Processing Systems 31},
    year = {2018}
}