Description
This is an attempt to make a wrapper class for a GAN in keras which can be used to abstract the whole architecture process.
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README
Simple GAN
This is my attempt to make a wrapper class for a GAN in keras which can be used to abstract the whole architecture process.
Overview
[alt text](assets/mnist_gan.png "GAN network using the MNIST dataset")
Flow Chart
Setting up a Generative Adversarial Network involves having a discriminator and a generator working in tandem, with the ultimate goal being that the generator can come up with samples that are indistinguishable from valid samples by the discriminator.
[alt text](assets/flow.jpg "High level flowchart")
Installation
pip install adversarials
Example
import numpy as np
from keras.datasets import mnist
from adversarials.core import Log
from adversarials import SimpleGAN
if __name__ == '__main__':
(X_train, _), (_, _) = mnist.load_data()
# Rescale -1 to 1
X_train = (X_train.astype(np.float32) - 127.5) / 127.5
X_train = np.expand_dims(X_train, axis=3)
Log.info('X_train.shape = {}'.format(X_train.shape))
gan = SimpleGAN(save_to_dir="./assets/images",
save_interval=20)
gan.train(X_train, epochs=40)
Documentation
Credits
- Understanding Generative Adversarial Networks - Noaki Shibuya
- Github Keras Gan
- Simple gan
Contribution
You are very welcome to modify and use them in your own projects.
Please keep a link to the original repository. If you have made a fork with substantial modifications that you feel may be useful, then please open a new issue on GitHub with a link and short description.
License (MIT)
This project is opened under the MIT 2.0 License which allows very broad use for both academic and commercial purposes.
A few of the images used for demonstration purposes may be under copyright. These images are included under the "fair usage" laws.
Todo
- Add view training(discriminator and generator) simultaneously using tensorboard
- Provision for Parallel data processing and multithreading
- Saving models to Protobuff files
- Using TfGraphDef and other things that could speed up training and inference
*Note that all licence references and agreements mentioned in the Simple GAN README section above
are relevant to that project's source code only.