This repository is a Tensorflow implementation of Alec Radford's Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, ICLR2016.
- tensorflow 1.9.0
- python 3.5.3
- numpy 1.14.2
- pillow 5.0.0
- pickle 0.7.4
- scipy 0.19.0
- matplotlib 2.0.2
- Generator
- Discriminator
- MNIST
- CIFAR10
- CelebA
MNIST and CIFAR10 dataset will be downloaded automatically if in a specific folder there are no dataset. Use the following command to download CelebA
dataset and copy the `CelebA' dataset on the corresponding file as introduced in Directory Hierarchy information.
python download2.py celebA
.
│ DCGAN
│ ├── src
│ │ ├── cache.py
│ │ ├── cifar10.py
│ │ ├── dataset.py
│ │ ├── dataset_.py
│ │ ├── download.py
│ │ ├── main.py
│ │ ├── solver.py
│ │ ├── tensorflow_utils.py
│ │ ├── utils.py
│ │ └── dcgan.py
│ Data
│ ├── celebA
│ ├── cifar10
│ └── mnist
src: source codes of the DCGAN
Implementation uses TensorFlow implementation to train the DCGAN. Same generator and discriminator networks are used as described in Alec Radford's paper, except that batch normalization of training mode is used in training and test mode that we found to get more stalbe results.
Use main.py
to train a DCGAN network. Example usage:
python main.py --is_train=true
gpu_index
: gpu index, default:0
batch_size
: batch size for one feed forward, default:256
dataset
: dataset name for choice [mnist|cifar10|celebA], default:mnist
is_train
: training or inference mode, default:False
learning_rate
: initial learning rate, default:0.0002
beta1
: momentum term of Adam, default:0.5
z_dim
: dimension of z vector, default:100
iters
: number of interations, default:200000
print_freq
: print frequency for loss, default:100
save_freq
: save frequency for model, default:10000
sample_freq
: sample frequency for saving image, default:500
sample_size
: sample size for check generated image quality, default:64
load_model
: folder of save model that you wish to test, (e.g. 20180704-1736). default:None
Use main.py
to evaluate a DCGAN network. Example usage:
python main.py --is_train=false --load_model=folder/you/wish/to/test/e.g./20180704-1746
Please refer to the above arguments.
@misc{chengbinjin2018dcgan,
author = {Cheng-Bin Jin},
title = {DCGAN-tensorflow},
year = {2018},
howpublished = {\url{https://github.com/ChengBinJin/DCGAN-TensorFlow}},
note = {commit xxxxxxx}
}
- This project borrowed some code from carpedm20
- Some readme formatting was borrowed from Logan Engstrom
Copyright (c) 2018 Cheng-Bin Jin. Contact me for commercial use (or rather any use that is not academic research) (email: sbkim0407@gmail.com). Free for research use, as long as proper attribution is given and this copyright notice is retained.