GANit

GANit is a Python library designed for the implementation and experimentation of Generative Adversarial Networks (GANs). It provides a modular and user-friendly framework for building, training, and evaluating various GAN architectures. The library aims to simplify the process of working with GANs, making them more accessible to researchers and developers.

The core of GANit lies in its flexible building blocks, allowing users to easily define and combine different components of a GAN, such as generators, discriminators, and loss functions. This modularity facilitates rapid prototyping and the exploration of novel GAN designs. GANit also includes built-in support for common optimization algorithms and training strategies, streamlining the training loop.

Key features of GANit include its ability to handle different data modalities, such as images and text, and its integration with popular deep learning frameworks like TensorFlow and PyTorch. The library offers utilities for data loading, preprocessing, and visualization, further enhancing the development workflow. GANit's design emphasizes clear code structure and comprehensive documentation, enabling users to understand and extend its functionalities. While still under development, GANit aims to be a valuable tool for anyone working with generative models.