The R interface to TensorFlow encompasses several packages each of which provide a different interface to the core TensorFlow engine. Several tools are available which can be used with any of these interfaces:

Installation Prior to using the R interface to TensorFlow you need to install a version of TensorFlow on your system. The install_tensorflow() function provides an easy to use wrapper for the various steps required to install either the CPU or GPU version of TensorFlow.
TensorBoard The computations you’ll use TensorFlow for - like training a massive deep neural network - can be complex and confusing. To make it easier to understand, debug, and optimize TensorFlow programs, a suite of visualization tools called TensorBoard is available. You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it.
Training Flags Tuning a model often requires exploring the impact of changes to many hyperparameters. The best way to approach this is generally not to progressively change your source code, but rather to define external flags for key parameters which you may want to vary. The flags() function provides a flexible mechanism for defining flags and varying them across training runs.
Training Runs The tfruns package provides a suite of tools for tracking and managing TensorFlow training runs and experiments from R. Track the hyperparameters, metrics, output, and source code of every training run, visualize the results of individual runs and comparisons between runs.