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This repository contains a straightforward implementation of Generative Adversarial Networks trained to fool a discriminator that sees real MNIST images, along with Mutual Information Generative Adversarial Networks (InfoGAN).

• Install tensorflow

Then run for GAN:

python3 infogan/__init__.py

And InfoGAN:

python3 infogan/__init__.py --infogan

To see samples from the model during training you can use Tensorboard as follows:

tensorboard --logdir MNIST_v1_log/

You should now see images like these show up:

On tensorboard you should see the following properties emerge:

Command line presented below will train Infogan for chairs dataset with the configuration that is supposed to uncover rotation as the continuous latent code. Download an untar dataset from here, and make sure that is is located at path/to/rendered_chairs/

python3 train.py --dataset path/to/rendered_chairs/ --scale_dataset 64 64 --batch_size 128 --discriminator conv:4:2:64:lrelu,conv:4:2:128:lrelu,conv:4:2:256:lrelu,conv:4:1:256:lrelu,conv:4:1:256:lrelu,fc:1024:lrelu --generator fc:1024,fc:8x8x256,reshape:8:8:256,deconv:4:1:256,deconv:4:2:256,deconv:4:2:128,deconv:4:2:64,deconv:4:1:1:sigmoid --categorical_lambda 1.0 --continuous_lambda 10.0 --categorical_cardinality 20 20 20 --num_continuous 1 --style_size 128 --plot_every 400 --force_grayscale

Note: generator architecture changed w.r.t. the publication, due to the fact that it was producing 32x32 images rather than 64x64 images as desired. Results may be different.