Programmer's Guide
The documents in this unit dive into the details of how TensorFlow works. The units are as follows:
High Level APIs
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Eager Execution, which is the easiest way to use tensorflow.
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Estimators, which introduces a high-level TensorFlow API that greatly simplifies ML programming.
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Importing Data, which explains how to set up data pipelines to read data sets into your TensorFlow program.
Low Level APIs
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Introduction, which introduces the basics of how you can use TensorFlow outside of the high Level APIs.
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Tensors, which explains how to create, manipulate, and access Tensors--the fundamental object in TensorFlow.
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Variables, which details how to represent shared, persistent state in your program.
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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.
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Save and Restore, which explains how to save and restore variables and models.
Accelerators
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Using GPUs explains how TensorFlow assigns operations to devices and how you can change the arrangement manually.
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Using TPUs explains how to modify
Estimator programs to run on a TPU.
ML Concepts
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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
TensorBoard
TensorBoard is a utility to visualize different aspects of machine learning. The following guides explain how to use TensorBoard:
Misc