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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.
https://www.tensorflow.org/programmers_guide/