TensorFlow Guide

The documents in this unit dive into the details of how TensorFlow works. The units are as follows:

High Level APIs

  • Keras, TensorFlow's high-level API for building and training deep learning models.
  • Eager Execution, an API for writing TensorFlow code imperatively, like you would use Numpy.
  • Importing Data, easy input pipelines to bring your data into your TensorFlow program.
  • Estimators, a high-level API that provides fully-packaged models ready for large-scale training and production.



  • Using GPUs explains how TensorFlow assigns operations to devices and how you can change the arrangement manually.
  • Using TPUs explains how to modify Estimator programs to run on a TPU.

Low Level APIs

  • Introduction, which introduces the basics of how you can use TensorFlow outside of the high Level APIs.
  • Tensors, which explains how to create, manipulate, and access Tensors--the fundamental object in TensorFlow.
  • Variables, which details how to represent shared, persistent state in your program.
  • Graphs and Sessions, which explains:
    • dataflow graphs, which are TensorFlow's representation of computations as dependencies between operations.
    • sessions, which are TensorFlow's mechanism for running dataflow graphs across one or more local or remote devices. If you are programming with the low-level TensorFlow API, this unit is essential. If you are programming with a high-level TensorFlow API such as Estimators or Keras, the high-level API creates and manages graphs and sessions for you, but understanding graphs and sessions can still be helpful.
  • Save and Restore, which explains how to save and restore variables and models.

ML Concepts

  • Embeddings, which introduces the concept of embeddings, provides a simple example of training an embedding in TensorFlow, and explains how to view embeddings with the TensorBoard Embedding Projector.



TensorBoard is a utility to visualize different aspects of machine learning. The following guides explain how to use TensorBoard:


Performance is an important consideration when training machine learning models. Performance speeds up and scales research while also providing end users with near instant predictions.

  • Performance overview contains a collection of best practices for optimizing your TensorFlow code.
  • Data input pipeline describes the tf.data API for building efficient data input pipelines for TensorFlow.
  • Benchmarks contain a collection of benchmark results for a variety of hardware configurations.

Additionally, TensorFlow Lite has optimization techniques for mobile and embedded devices.


This section explains how developers can add functionality to TensorFlow's capabilities.

  • TensorFlow architecture presents an architectural overview.
  • Create an op, which explains how to create your own operations.
  • Custom filesystem plugin, which explains how to add support for your own shared or distributed filesystem.
  • Custom file and record formats, which details how to add support for your own file and record formats.
  • Language bindings, Python is currently the only language supported by TensorFlow's API stability promises. However, TensorFlow also provides functionality to create or develop features in other languages.
  • Model files, for creating tools compatible with TensorFlow's model format.

XLA (Accelerated Linear Algebra) is an experimental compiler for linear algebra that optimizes TensorFlow computations.