from kan import KAN import torch # Create a KAN with 2 inputs, 5 hidden neurons, and 1 output model = KAN(width=[2, 5, 1], grid=5, k=3) # Training follows a standard loop structure # model.train(dataset, opt="LBFGS", steps=20) Use code with caution. Copied to clipboard
: Because the functions are univariate splines, they are easier for humans to visualize and understand, making KANs particularly useful for AI for Science . The pykan Library
The pykan repository, maintained by the original researchers, provides the tools to build, train, and visualize these networks.
For more technical details and community discussions, you can explore the Annotated KAN blog or the official GitHub repository .