/Tensorflow-GANs-Architectures-Implementation

Tensorflow implementation of some generative adversarial networks architectures.

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

Tensorflow GANs Architectures Implementation

Brief Description

GANs have proven to be very powerful generative models. So, here's a well-structured Tensorflow project containing implementations of some GANs architectures.

Utilized Frameworks

  • Tensorflow 1.13.1

Repository Strucuture

1) base folder:

  • contains abstract classes for both model and trainer.

2) configs folder:

  • contains json files for different model configurations.

3) data folder:

  • for the training data to be added.

4) data_loader folder:

  • contains data generator class for data loading and preprocessing.

5) models folder:

  • contains different model implementations.

6) trainers folder:

  • contains trainers for models.

7) utils folder:

  • contains logger for Tensorboard summary, argument parser, configuration processing and directory creation.

Implemented Architectures

Usage

  • Put your training images in data folder.
  • Edit the configuration JSON in configs folder (optional).
  • Run the main file providing config and model arguments:
    python main.py -c <config_path> -m <model_name>
  • Have a nice day!

To-Do List

  1. Implement more GANs architectures.
  2. Add Tensorflow 2.0 compatibility.
  3. Add distributed training to the trainer process.
  4. Improve the current training process and fix some issues.

Acknowledgment