Skip to content

TensorFlow implementation of Deep Convolutional Generative Adversarial Networks

License

Notifications You must be signed in to change notification settings

drewszurko/tensorflow-DCGAN

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

TensorFlow DCGAN

Usage

Train a model.
$ python main.py --dataset='celeb_a' --height=64 --width=64 output_dir=''

Options.

  --batch_size: Batch size of images to train.
    (default: '64')
    (an integer)
  --beta: Training beta.
    (default: '0.5')
    (a number)
  --cache: Optional: [None, memory, disk]. If specified, data will be cached
    for faster training. 
    memory: slower, disposable. 
    disk: faster, requires space.
    (default: '')
  --[no]crop: Center crop image.
    (default: 'false')
  --dataset: Dataset to train [cifar10, celeb_a, tf_flowers]
    (default: 'cifar10')
  --epochs: Number of epochs to train.
    (default: '100')
    (an integer)
  --height: Image input height.
    (default: '64')
    (an integer)
  --width: Image input width.
    (default: '64')
    (an integer)
  --learning_rate: Learning rate.
    (default: '0.0002')
    (a number)
  --output_dir: Directory to save output.
    (default: '')

Results

CelebA
Generated image of CelebA dataset Generated gif of CelebA dataset

CelebA cropped
Generated image of CelebA dataset Generated gif of CelebA dataset

Flowers ~3,600 images
Generated image of Flower dataset

About

TensorFlow implementation of Deep Convolutional Generative Adversarial Networks

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages