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.
- Python 2.7 or Python 3.3+
- Tensorflow
- SciPy
- (Optional) Align&Cropped Images.zip : Large-scale CelebFaces Dataset
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
After 6th epoch:
With custom dataset (with high noises):
More results can be found here.
Taehoon Kim / @carpedm20