strohmfn / mnist-gan

MNIST-GAN

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.

Losses after 100 epochs

Result after 100 epochs

Randomly generated images after 100 epochs of training. The generated numbers are clearly recognizable and diverse.

Getting Started

You can view the notebook here on github.

Run the notebook

Prerequisites

• Python 3
• Tensorflow
• Keras
• Jupyter

Starting the notebook

Simply open a new terminal in the directory and type:

> jupyter notebook

Setup model

make sure you run all codeblocks from top to bottom to setup the network

Running the tests

To test the model, you only need to run the last codeblock. This will evaluate the model and print the accuracy for each testset.

Built With

• Keras - The framework to create the model
• Project Jupyter - Nice and easy python notebooks