Neural Tangents: Fast and Easy Infinite Neural Networks in Python
This library facilitates research in machine learning by making it easier to study infinite-width networks, which is incremental as it builds on existing theoretical work.
The paper introduces Neural Tangents, a library that enables research into infinite-width neural networks by providing tools for training and evaluating them analytically or via gradient descent, with support for CPU, GPU, and TPU.
Neural Tangents is a library designed to enable research into infinite-width neural networks. It provides a high-level API for specifying complex and hierarchical neural network architectures. These networks can then be trained and evaluated either at finite-width as usual or in their infinite-width limit. Infinite-width networks can be trained analytically using exact Bayesian inference or using gradient descent via the Neural Tangent Kernel. Additionally, Neural Tangents provides tools to study gradient descent training dynamics of wide but finite networks in either function space or weight space. The entire library runs out-of-the-box on CPU, GPU, or TPU. All computations can be automatically distributed over multiple accelerators with near-linear scaling in the number of devices. Neural Tangents is available at www.github.com/google/neural-tangents. We also provide an accompanying interactive Colab notebook.