/TensorFlow Guide

Programmer's Guide

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

High Level APIs

  • Eager Execution, which is the easiest way to use tensorflow.
  • Estimators, which introduces a high-level TensorFlow API that greatly simplifies ML programming.
  • Importing Data, which explains how to set up data pipelines to read data sets into your TensorFlow program.

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.


  • 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.

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:


© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.