Building & Training GAN Model From Scratch In Python
Develop Your Own Generative Adversarial Networks with This Complete Python Tutorial
GANs are a powerful type of generative model that can synthesize new and realistic images. By walking through the complete implementation, readers will gain a solid understanding of how GANs work behind the scenes.
The tutorial begins by importing necessary libraries and loading the Fashion-MNIST dataset that will be used to train the GAN. Code samples are then presented to build the core components of a GAN — the generator and discriminator models.
Further sections explain how to construct a combined model that trains the generator to fool the discriminator, as well as how to design a training function that optimizes the adversarial process.
This is the fifth article in this series. In the first article, we discussed how GANs work. In the second article, we explored how GANs are trained. In the third article, we discussed how GANs-generated images are evaluated. In the fourth article, I gave an overview of the popular GANs applications and models. In the coming article, we explore how to deploy a trained GAN model using Streamlit and Heroku.
Table of Contents:
Importing Libraries & Downloading Dataset
Building the Generator Model
Building the Discriminator Model
Building the Combined Model
Building the Training Function
Train & Observe the Results
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