leconteur/DCGAN-tensorflow

A tensorflow implementation of Deep Convolutional Generative Adversarial Networks

DCGAN in Tensorflow

Tensorflow implementation of Deep Convolutional Generative Adversarial Networks which is a stabilize Generative Adversarial Networks. The referenced torch code can be found here.

To avoid the fast convergence of D (discriminator) network, G (generatior) network is updatesd twice for each D network update which is a different from original paper.

Prerequisites

• Python 2.7 or Python 3.3+
• Tensorflow
• SciPy
• (Optional) Align&Cropped Images.zip : Large-scale CelebFaces Dataset

Usage

First, download dataset with:

$ mkdir data
$ python download.py --datasets celebA

To train a model with celebA dataset:

$ python main.py --dataset celebA --is_train True --is_crop True

To test with an existing model:

$ python main.py --dataset celebA --is_crop True

Or, you can use your own dataset (without central crop) by:

$ mkdir data/DATASET_NAME
... add images to data/DATASET_NAME ...
$ python main.py --dataset DATASET_NAME --is_train True
$ python main.py --dataset DATASET_NAME

Results

After 6th epoch:


More results can be found here.

Author

Taehoon Kim / @carpedm20