This is a simple Keras implementation of a generative adversial network that is trained to generate images of numbers similar to images in the MNIST dataset.
Randomly generated images after 100 epochs of training.
The generated numbers are clearly recognizable and diverse.
You can view the notebook here on github.
- Python 3
- Tensorflow
- Keras
- Jupyter
Simply open a new terminal in the directory and type:
> jupyter notebook
make sure you run all codeblocks from top to bottom to setup the network
To test the model, you only need to run the last codeblock. This will evaluate the model and print the accuracy for each testset.
- Keras - The framework to create the model
- Project Jupyter - Nice and easy python notebooks