Research Project: Building a Generative Adversarial Network

Face generated by a GAN

Goals and Overview

A Generative Adversarial Network (GAN) is a type of neural network for generative modeling. The generative model is trained using two neural network models: a generator and a discriminator. GANs are complex so to put them in laymen’s terms, the way they work is there is a generator model that learns to generate plausible samples from a dataset. There is also a discriminator set that is provided with the same dataset and it’s goal is to distinguish real data from fake data (data that is produced by the generator model). Both models enter a game, so to speak, where the generator seeks to fool the discriminator by learning and generating data that is so similar to the shared dataset that the discriminator is unable to distinguish generated data from actual data. This game that the models play is where the word “Adversarial” comes from in the network name.

In this research project, I sought to learn about and become more familiar with GANs, and, in particular, image producing GANs. Because GANs are very computationally heavy, the MNIST dataset was selected for because it is a smaller, simpler dataset and thus less computationally taxing.

Practical Application of GANs

Artists can use GANs to create new images


When training the model, it is not usually possible to feed all of the data into the algorithm at one time because of the memory limitations and data sizes so the data must be broken into smaller pieces. This was certainly the case for my project even though I purposely chose a simple and small dataset to work with. Here are two key terms that were important to understanding how GANs work:

Generated Images

Here is an example of some of the digits that the discriminator model was trained on.

The image above shows digits that have been produced by the generator model after 10 epochs. At this point, the generator has learned from the discriminator where to centralize crude digits.

After 50 epochs, the digits have gained more “curve” and begin to look more like digits.

After 100 epochs, even more smoothening has happened and the figures are resembling more and more like actual digits.

Future Work

For my future work, I would like to create a GAN to generate faces. The added complexity of the images will pose new challenges with which to improve my knowledge of GANs. GANs are very complicated so I’m aiming to learn more about how they work on the backend. I hope to use this new technology in my field of work if ever it is applicable.

Here is the link to my work on GitHub:

Thanks for reading and best of luck to you,


Data Scientist | Health Enthusiast | Learner

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