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Code for UAI'19: Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning
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models
pretrained
.gitignore
124_supplement.pdf
README.md
configurations.json
datasets.py
download_preprocess_data.py
eval_rat_spn_discriminative.py
eval_rat_spn_generative.py
install_tensorflow_venv.sh
quick_eval_rat_spn_discriminative.py
quick_eval_rat_spn_generative.py
quick_run_rat_spn_generative.py
quick_run_rat_spn_mnist.py
run_rat_spn_fashion_mnist.py
run_rat_spn_generative.py
run_rat_spn_higgs.py
run_rat_spn_imdb.py
run_rat_spn_mnist.py
run_rat_spn_theorem.py
run_rat_spn_wine.py
test_rat_spn.py
train_rat_spn.py
utils.py

README.md

RAT-SPN

Code for UAI'19: Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

V0.2

  • RAT-SPN model
  • Experiments for generative learning of RAT-SPNs using EM
  • Experiments for discriminative learning of RAT-SPNs using Adam

Setup

git clone https://github.com/cambridge-mlg/RAT-SPN

cd RAT-SPN

./install_tensorflow_venv.sh

source ratspn_venv/bin/activate

python download_preprocess_data.py

Quick Run for Generative Experiments

This will simply train a single RAT-SPN (no crossvalidation).

python quick_run_rat_spn_generative.py

python quick_eval_rat_spn_generative.py

Quick Run for Discriminative Training on MNIST

This will simply train a single RAT-SPN for each depth.

python quick_run_rat_spn_mnist.py

python quick_eval_rat_spn_discriminative.py

Full Training

See the run_.py and eval_.py files

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