https://github.com/lucko515/generative-adversarial-network
This is the implementation of simple GAN using TensorFlow as a framwork.
https://github.com/lucko515/generative-adversarial-network
deep-learning generative-adversarial-network generative-models tensorflow
Last synced: 11 months ago
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This is the implementation of simple GAN using TensorFlow as a framwork.
- Host: GitHub
- URL: https://github.com/lucko515/generative-adversarial-network
- Owner: lucko515
- Created: 2017-08-12T18:45:43.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2020-01-07T20:24:59.000Z (over 6 years ago)
- Last Synced: 2025-04-08T11:05:49.873Z (about 1 year ago)
- Topics: deep-learning, generative-adversarial-network, generative-models, tensorflow
- Language: Jupyter Notebook
- Size: 123 KB
- Stars: 4
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Generative Adversarial Networks
Here I have implemented simple version of GAN using TensorFlow framework. The testing has been done on MNIST dataset.
*The structure of this network looks like the picture below.*
## Dataset
The dataset used in this mini-project is MNIST hand written digits dataset. You can import it into your project by using TensorFlow built-in functions.
## Dependencies
> * [Numpy](http://www.numpy.org) 1.10.4
> * [Matplotlib](https://matplotlib.org) 1.5.1
> * [TensorFlow](https://www.tensorflow.org) 1.2.0
## Python version
The Python version that I have used in this notebook is 3.5. But 3.6 is supported as well.
## Code
Whole code for this project can be found inside **GAN.ipynb**.
## Run
To run this project you will need some software, like Anaconda, which provides support for running .ipynb files (Jupyter Notebook).
After making sure you have that, you can run from a terminal or cmd next lines:
`ipython notebook GAN.ipynb`
or
`jupyter notebook GAN.ipynb`
## License
IT License
Copyright (c) 2017 Luka Anicin
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.