This repository provides a reference implementation for learning Flow-GAN models as described in the paper:
Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models
Aditya Grover, Manik Dhar, and Stefano Ermon.
AAAI Conference on Artificial Intelligence (AAAI), 2018.
Paper: https://arxiv.org/pdf/1705.08868.pdf
Blog post: https://ermongroup.github.io/blog/flow-gan
The codebase is implemented in Python 3.6. To install the necessary requirements, run the following commands:
pip install -r requirements.txt
The scripts for downloading and loading the MNIST and CIFAR10 datasets are included in the datasets_loader folder. These scripts will be called automatically the first time the main.py script is run.
Learning and inference of Flow-GAN models is handled by the main.py script which provides the following command line arguments.
--beta1 FLOAT beta1 parameter for Adam optimizer
--epoch INT number of epochs to train
--batch_size FLOAT training batch size
--learning_rate FLOAT learning rate
--input_height INT The size of image to use
--input_width INT The size of image to use if none given use same value as input height
--c_dim INT Dimension of image color
--dataset STR The name of dataset [mnist, svhn, cifar-10]
--checkpoint_dir STR Directory name to save the checkpoints
--log_dir STR Directory name to save the logs
--sample_dir STR Directory name to save the image samples
--f_div STR divergence used for specifying the gan objective
--prior STR prior for generator
--alpha FLOAT alpha value for applying logits
--lr_decay FLOAT Learning rate decay rate
--min_lr FLOAT minimum lr allowed on decay
--reg FLOAT regularization parameter for adversarial training
--model_type STR real_nvp or nice
--n_critic INT no of discriminator iterations
--no_of_layers INT No of units between input and output in the m function for a coupling layer
--hidden_layers INT Size of hidden layers (applicable only for NICE)
--like_reg FLOAT regularizing factor for likelihood vs. adversarial losses for hybrid
--df_dim FLOAT Dim depth for discriminator
Maximum Likelihood Estimation (MLE)
python main.py --dataset mnist --input_height=28 --c_dim=1 --checkpoint_dir checkpoint_mnist/mle --sample_dir samples_mnist/mle --model_type nice --log_dir logs_mnist/mle
--prior logistic --beta1 0.5 --learning_rate 1e-4 --alpha 1e-7 --epoch 500 --batch_size 100 --like_reg 1.0 --n_critic 0 --no_of_layers 5
Adversarial training (ADV)
python main.py --dataset mnist --input_height=28 --c_dim=1 --checkpoint_dir checkpoint_mnist/gan --sample_dir samples_mnist/gan --model_type nice --log_dir logs_mnist/gan
--prior logistic --beta1 0.5 --learning_rate 1e-4 --alpha 1e-7 --reg 10.0 --epoch 500 --batch_size 100 --like_reg 0.0 --n_critic 5 --no_of_layers 5
Hybrid
python main.py --dataset mnist --input_height=28 --c_dim=1 --checkpoint_dir checkpoint_mnist/flow --sample_dir samples_mnist/flow --model_type nice --log_dir logs_mnist/flow
--prior logistic --beta1 0.5 --learning_rate 1e-4 --alpha 1e-7 --reg 10.0 --epoch 500 --batch_size 100 --like_reg 1.0 --n_critic 5 --no_of_layers 5
Maximum Likelihood Estimation (MLE)
python main.py --dataset cifar --input_height=32 --c_dim=3 --checkpoint_dir checkpoint_cifar/mle --sample_dir samples_cifar/mle --model_type real_nvp --log_dir logs_cifar/mle
--prior gaussian --beta1 0.9 --learning_rate 1e-3 --alpha 1e-7 --epoch 300 --lr_decay 0.999995 --batch_size 64 --like_reg 1.0 --n_critic 0 --no_of_layers 8 --batch_norm_adaptive 0
Adversarial training (ADV)
python main.py --dataset cifar --input_height=32 --c_dim=3 --checkpoint_dir checkpoint_cifar/gan --sample_dir samples_cifar/gan --model_type real_nvp --log_dir logs_cifar/gan
--prior gaussian --beta1 0.5 --learning_rate 1e-4 --alpha 1e-7 --epoch 300 --batch_size 64 --like_reg 0.0 --n_critic 5 --no_of_layers 8
Hybrid
python main.py --dataset cifar --input_height=32 --c_dim=3 --checkpoint_dir checkpoint_cifar/flow --sample_dir samples_cifar/flow --model_type real_nvp --log_dir logs_cifar/flow
--prior gaussian --beta 0.5 --learning_rate 1e-3 --lr_decay 0.99999 --alpha 1e-7 --epoch 500 --batch_size 64 --like_reg 20. --n_critic 5 --no_of_layers 8
Portions of the codebase in this repository uses code originally provided in the open-source DCGAN and Real-NVP repositories.
If you find flow-GANs useful in your research, please consider citing the following paper:
@inproceedings{grover2018flowgan,
title={Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models},
author={Grover, Aditya and Dhar, Manik and Ermon, Stefano},
booktitle={AAAI Conference on Artificial Intelligence},
year={2018}}
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