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.
- 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.
- 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.)
