This is a pytorch implementation of InfoGAN.

This repository has the following features that others do not have:

• Highly customizable.

  • You can use this for your own dataset, settings.
  • Most parameters including the latent variable design can be customized by editing the yaml config file.

• OK clean, structured codes

  • This is totally my personal point of view. 😉

• TensorBoard is available by default.

• latent variable design
  • z ~ N(0, 1), 64 dimensions
  • c1 ~ Cat(K=10, p=0.1)
  • c2, c3, c4 ~ N(0, 1)
• batchsize: 300, epochs: 500
• mnist.yaml

c1 (digit type)	c2 (rotation)

c3 (line thickness)	c4 (digit width)

• Python (~3.6)

make setup

• Start training

  python src/train.py --config <config.yaml>

  You need to specify all of training settings with yaml fromat. Example files are placed under configs/.

  If you want to try training anyway, my configuration for debugging is available.

  make debug

• Open tensorboard

  make tb

  Training metrics (ex. loss) are printed on console and tensorboard.

  By default, tensorboard watches ./results directory. To change the path, execute tensorboard --logdir <path> or edit Makefile.

• upload result on MNIST dataset.
• upload result on Fashion-MNIST dataset.
• automatic hyper-parameters tuning with Optuna.