/TensorFlow Guide

Programmer's Guide

The documents in this unit dive into the details of writing TensorFlow code. For TensorFlow 1.3, we revised this document extensively. The units are now as follows:

  • Estimators, which introduces a high-level TensorFlow API that greatly simplifies ML programming.
  • 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.
  • Saving and Restoring, which explains how to save and restore variables and models.
  • Input Pipelines, which explains how to set up data pipelines to read data sets into your TensorFlow program.
  • 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.
  • Debugging TensorFlow Programs, which explains how to use the TensorFlow debugger (tfdbg).
  • TensorFlow Version Compatibility, which explains backward compatibility guarantees and non-guarantees.
  • FAQ, which contains frequently asked questions about TensorFlow. (We have not revised this document for v1.3, except to remove some obsolete information.)

© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.