google-deepmind/sonnet

Documentation | Examples Sonnet is a library built on top of TensorFlow 2 designed to provide simple, composable abstractions for machine learning research. Sonnet has been designed and built by researchers at DeepMind. It can be used to construct neural networks for many different purposes (un/supervised learning, reinforcement learning, ...). We find it is a successful abstraction for our organization, you might too! More specifically, Sonnet provides a simple but powerful programming model centered around a single concept: snt.Module. Modules can hold references to parameters, other modules and methods that apply some function on the user input. Sonnet ships with many predefined modules (e.g. snt.Linear, snt.Conv2D, snt.BatchNorm) and some predefined networks of modules (e.g. snt.nets.MLP) but users are also encouraged to build their own modules. Unlike many frameworks Sonnet is extremely unopinionated about how you will use your modules. Modules are designed to be self contained and entirely decoupled from…