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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:

  • @{$programmers_guide/estimators$Estimators}, which introduces a high-level TensorFlow API that greatly simplifies ML programming.
  • @{$programmers_guide/tensors$Tensors}, which explains how to create, manipulate, and access Tensors--the fundamental object in TensorFlow.
  • @{$programmers_guide/variables$Variables}, which details how to represent shared, persistent state in your program.
  • @{$programmers_guide/graphs$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.
  • @{$programmers_guide/saved_model$Saving and Restoring}, which explains how to save and restore variables and models.
  • @{$programmers_guide/datasets$Input Pipelines}, which explains how to set up data pipelines to read data sets into your TensorFlow program.
  • @{$programmers_guide/threading_and_queues$Threading and Queues}, which explains TensorFlow's older system for multi-threaded, queue-based input pipelines. Beginning with TensorFlow 1.2, we recommend using the tf.contrib.data module instead, which is documented in the "Input Pipelines" unit.
  • @{$programmers_guide/embedding$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.
  • @{$programmers_guide/debugger$Debugging TensorFlow Programs}, which explains how to use the TensorFlow debugger (tfdbg).
  • @{$programmers_guide/version_compat$TensorFlow Version Compatibility}, which explains backward compatibility guarantees and non-guarantees.
  • @{$programmers_guide/faq$FAQ}, which contains frequently asked questions about TensorFlow. (We have not revised this document for v1.3, except to remove some obsolete information.)