gwg_release's Introduction

GWG_release

Official release of code for "Oops I Took A Gradient: Scalable Sampling for Discrete Distributions" which has been accepted for a long presentation to ICML 2021.

The paper is by me, Kevin Swersky, Milad Hashemi, David Duvenaud, and Chris Maddison

See Gibbs-With-Gradients sampling from an Ising model:

Code for sampling experiments can be found in:

rbm_sample.py, ising_sample.py, fhmm_sample.py, potts_sample.py, svgd_sample.py

To generate training data for ising inference experiments run:

./generate_data.sh

Datasets for EBM training can be found at:

https://github.com/jmtomczak/vae_vampprior/tree/master/datasets

Download them and unzip as:

GWG_release/

    datasets/
        Caltech...
        FreyFaces...
        Histo...
        MNIST_static/
        Omniglot/

If you would like access to the protein data please contact me at [email protected], they are quite large and don't fit here :(

To train a binary EBM run:

python pcd_ebm_ema.py --save_dir $DIR} \
    --sampler gwg --sampling_steps $NUM_STEPS --viz_every 100 \
    --model resnet-64 --print_every 10 --lr .0001 --warmup_iters 10000 --buffer_size 10000 --n_iters 50000 \
    --buffer_init mean --base_dist --reinit_freq 0.0 \
    --eval_every 5000 --eval_sampling_steps 10000 &

To train a categorical EBM run:

python pcd_ebm_ema_cat.py --save_dir $DIR \
          --sampler gwg --sampling_steps $NUM_STEPS --viz_every 100 \
          --model resnet-64 --proj_dim $PROJ_DIM --print_every 10 --lr .0001 --warmup_iters 10000 --buffer_size 1000 \
          --n_iters 50000 \
          --buffer_init mean --base_dist --p_control 0.0 --reinit_freq 0.0 \
          --eval_every 5000 --eval_sampling_steps 10000 --dataset_name ${DATA}

To evaluate with AIS run:

This takes a while...

python eval_ais.py \
    --ckpt_path $CKPT_path \
    --save_dir $DIR \
    --sampler gwg --model resnet-64 --buffer_size 10000 \
    --n_iters 300000 --base_dist --n_samples 500 \
    --eval_sampling_steps 300000 --ema --viz_every 1000

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