deep GAN

deep GAN experiment.

Main idea

Multiple generators cooperate to improve generative ability under the competitive selection of one discriminator

Requirement

• tensorflow
• numpy

Usage

Step 1. Clone this repository and adjust the environment setting in main.py if necessary.

Ensure your system is installed with Git and clone this reposity with command line:

$ git clone https://github.com/naturomics/deepGAN.git

cd deepGAN and edit file 'main.py' to suit your configuration.

Step 2. Download MNIST dataset, mv and extract them into data/mnist directory.

$ mkdir -p data/mnist
$ cd data/mnist
$ wget -c http://yann.lecun.com/exdb/mnist/{train-images-idx3-ubyte.gz,train-labels-idx1-ubyte.gz,t10k-images-idx3-ubyte.gz,t10k-labels-idx1-ubyte.gz}
$ gunzip *.gz
$ cd -   # go back to project root directory

Step 3. Start to run for training with command line:

$ python main.py --dataset mnist --input_height=28 --output_height=28 --is_train

Results

Training Loss

Experiments were carried out with different hyper parameters theta and beta

The legend for various hyper parameters(theta00 meaning theta=0.0 and no using beta, theta04beta08 i.e. theta=0.4 and beta=0.8, etc.):

d1 total loss:

d2 total loss:

d loss with g1 as fake input:

d loss with g2 as fake input:

g1 loss:

g2 loss: