- run_gan.py
- gan_models/config.py
- gan_models/model.py
- gan_models/generator.py
- gan_models/discriminator.py
- run_vae.py
- vae_models/config.py
- vae_models/model.py
- vae_models/encoder.py
- vae_models/decoder.py
- Choose dataset and set hyperparamters in
run_gan.py
orrun_vae.py
MNIST, Affined MNIST, Fashion-MNIST and CIFAR10 are supported by default.
Our data loader automatically download dataset and offers batch sampling method `next_batch()`.
See '{DATASET_NAME}.py' scrips in project root folder for detail, or see `gan_models/train.py` and `vae_models/train.py` for usage example.
- Run it.
- Define your function in
gan_models/discriminator.py
organ_models/generator.py
- Open
run_gan.py
and setgenerator
ordiscriminator
argument to the name of your new function.
- Define your function in
vae_models/encoder.py
orvae_models/decoder.py
- Open
run_vae.py
and setencoder
ordecoder
argument to the name of your new function.
- Make a new model class that inherits
gan_models/model.py
orvae_models/model.py
and place it ingan_models
orvae_models
. - If you want, make new discriminator, generator, encoder or decoder as guided above.
Supports MNIST, Affined MNIST, Fashion-MNIST and CIFAR10. train classification model using
inception_score/train_{DATASET}_classifier.py
Evaluate inception score using
inception_score/eval_{DATASET}.py
Classification models are defined in
inception_score/model_{DATASET}.py