Generative Adversarial Nets

PyTorch implementation of Generative Adversarial Nets.

• images source

Requirements

• Python 3.6
• PyTorch
• hb-config (Singleton Config)
• Slack Incoming Webhook URL

Project Structure

.
├── config                  # Config files (.yml, .json) using with hb-config
├── gan                     # Generative Adversarial Networks architecture 
    ├── __init__.py             # train, evaluate, predict logic
    ├── module.py               # Discriminator, Generator module
    └── utils.py                # Save and Load Model, TensorBoard
├── data_loader.py          # make_data_loader (using DataLoader)
├── main.py                 
├── model.py                # define Model Spec
└── model.py                # utils

Reference : hb-config

• Manage experiments like tf.Estimator

Config

Can control all Experimental environment.

example: config.yml

data:
  path: "data/"

model:
  z_dim: 100     # Random noise dimension coming into generator, per output vector
  real_dim: 784

  g_h1: 256
  g_h2: 512
  g_h3: 1024

  d_h1: 1024
  d_h2: 512
  d_h3: 256

  dropout: 0.3

train:
  model_dir: "logs/gan"
  batch_size: 64
  train_steps: 50000

  d_learning_rate: 0.0002  # 2e-4
  g_learning_rate: 0.0002
  optim_betas:
    - 0.9
    - 0.999

  save_checkpoints_steps: 1000
  verbose_step_count: 100

predict:
  batch_size: 64

slack:
  webhook_url: ""  # after training notify you using slack-webhook

Usage

Install requirements.

pip install -r requirements.txt

Then, start training

python main.py --mode train

After training, generate images

python main.py --mode predict

• generated image example

Experiments modes

✅ : Working
◽ : Not tested yet.

• ◽ evaluate : Evaluate on the evaluation data.
• ✅ train : Fit the estimator using the training data.
• ◽ train_and_evaluate : Interleaves training and evaluation.
• ✅ predict : Generate images.

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Tensorboar

tensorboard --logdir logs

• example

Reference

• hb-research/notes - Generative Adversarial Nets
• Paper - Generative Adversarial Nets
• pytorch-tutorial

Author

Dongjun Lee ([email protected])