Training Keras Models using the Rust TensorFlow Bindings

Rust has become increasingly popular. Its safe execution and super fast runtime, combined with a strong community support, have made it an attractive alternative to languages like C. With little overhead it is possible to run Rust in production on micro-devices and, in the context of Edge Computing, might be a good choice when deploying Neural Networks at Edge. While there are many examples available to use pre-trained TensorFlow models with the Rust bindings or the TensorFlow-C API, there is little or none available on how to actually train models directly in Rust. Therefore in this brief tutorial I will outline a way to do so. For this demonstration we will create a very simple model, that merely receives a tensor with two elements and a single value as a target.