ritheshkumar95 / Energy_based_generative_models
PyTorch code accompanying our paper on Maximum Entropy Generators for Energy-Based Models
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Maximum Entropy Generators for Energy-Based Models
All experiments have tensorboard visualizations for samples / density / train curves etc.
- To run the toy data experiments:
python scripts/train/ebm_toy.py --dataset swissroll --save_path logs/swissroll
- To run the discrete mode collapse experiment:
python scripts/train/ebm_mnist.py --save_path logs/mnist_3 --n_stack 3
This requires the pretrained mnist classifier:
python scripts/train/mnist_classifier.py
- To run the CIFAR image generation experiment:
python scripts/train/ebm_cifar.py --save_path logs/cifar
To run the MCMC evalulations on CIFAR data:
python scripts/test/eval_metrics_cifar --load_path logs/cifar --n_samples 50000 --mcmc_iters 5 --temp .01
NOTE: This requires cloning the TTUR repo in the current working directory (https://github.com/bioinf-jku/TTUR).
- To run the CelebA image generation experiments:
python scripts/train/ebm_celeba.py --save_path logs/celeba
NOTE: Results are subject to PyTorch version. I have already noticed variance in quantitative numbers with PyTorch version upgrades.
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