https://github.com/hcnoh/wgan-tensorflow2

TensorFlow2 Implementation for Wasserstein GAN
https://github.com/hcnoh/wgan-tensorflow2

deep-learning neural-networks tensorflow2 tensorflow2-experiments tensorflow2-models

Last synced: 7 months ago
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TensorFlow2 Implementation for Wasserstein GAN

• Host: GitHub
• URL: https://github.com/hcnoh/wgan-tensorflow2
• Owner: hcnoh
• Created: 2019-10-23T09:41:14.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-12-07T09:59:36.000Z (over 6 years ago)
• Last Synced: 2025-03-30T15:46:50.458Z (about 1 year ago)
• Topics: deep-learning, neural-networks, tensorflow2, tensorflow2-experiments, tensorflow2-models
• Language: Jupyter Notebook
• Homepage:
• Size: 32.7 MB
• Stars: 7
• Watchers: 2
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          
# WGAN Implementation for Generating MNIST Images in TensorFlow2

This repository is for the TensorFlow2 implementation for WGAN. This repository provides the training module and Jupyter notebook for testing a generation of the trained models. MNIST dataset was used for this repository.

![](/assets/img/README/README_2019-12-07-18-50-47.png)

*The result is not so good...*

## Install Dependencies

1. Install Python 3.5.2.

2. Install TensorFlow ver 2.0.0. If you can use a GPU machine, install the GPU version of TensorFlow, or just install the CPU version of it.

3. Install Python packages(Requirements). You can install them simply by using following bash command.

    ```bash

    $ pip install -r requirements

    ```

    You can use `Virtualenv`, so that the packages for requirements can be managed easily. If your machine have `Virtualenv` package, the following bash command would be useful.

    ```bash

    $ virtualenv wgan-tf2-venv

    $ source ./wgan-tf2-venv/bin/activate

    $ pip install -r requirements.txt

    ```

## Training

*Note: TensorFlow provides dataset modules for some well known datasets such as MNIST, CIFAR-10 etc. In this repository, the only usage for TensorFlow MNIST dataset module was implemented yet. Usages for other datasets will be implemented too.*

1. **Modify the path for dataset in `config.py`.**

2. **Modify the path for directory for saving model checkpoint.**

3. **Execute training process by `train.py`.**

## Checking Results and Testing Generation

The Jupyter notebook for checking results and testing the image generation is provided. Please check `result_plot.ipynb`.

## Results

1. **Ploting the Generator and Discriminator Losses**

    ![](/assets/img/README/README_2019-12-07-18-54-05.png)

2. **Image Generation Results**

    ![](/assets/img/result_plot/image_generation_result_changes.gif)

## References

- GAN: [Generative Adversarial Nets](http://papers.nips.cc/paper/5423-generative-adversarial-nets)

- WGAN : [Wasserstein GAN

](https://arxiv.org/abs/1701.07875)

## Author

Hyungcheol Non / [About Me](https://hcnoh.github.io/about)