#Evaluation metrics

TF-GAN is a versatile library that simplifies the process of training and evaluating Generative Adversarial Networks (GANs). Easily installable via pip, it offers seamless integration with existing workflows through TF-GAN calls, custom scripts, or other frameworks. Its modular components include Core for foundational training support, Features for standard GAN operations, Losses such as Wasserstein, and Evaluation metrics like Inception Score and Frechet Distance. TF-GAN is utilized in various Google projects and supports numerous research initiatives. It accommodates different GAN configurations and offers flexibility in model training, making it accessible for a broad audience, from academic researchers to industry professionals.

The toolkit provides over 35 metrics across six categories to quantitatively evaluate neural network explanations. It supports models from PyTorch and TensorFlow, helping ensure explanations align with responsible AI standards. Suitable for various data types and methods, the toolkit fosters a reliable evaluation environment. Join its active development on Discord and explore features like EfficientMPRT and SmoothMPRT. Tutorials and installation guides are available for integration into AI projects.