The documents in this unit dive into the details of how TensorFlow works. The units are as follows:
High Level APIs
- Keras, TensorFlow's high-level API for building and training deep learning models.
- Eager Execution, an API for writing TensorFlow code imperatively, like you would use Numpy.
- Estimators, a high-level API that provides fully-packaged models ready for large-scale training and production.
- Importing Data, easy input pipelines to bring your data into your TensorFlow program.
- Estimators, learn how to use Estimators for machine learning.
- Premade Estimators, the basics of premade Estimators.
- Checkpoints, save training progress and resume where you left off.
- Feature Columns, handle a variety of input data types without changes to the model.
- Datasets for Estimators, use
tf.datato input data.
- Creating Custom Estimators, write your own Estimator.
- Using GPUs explains how TensorFlow assigns operations to devices and how you can change the arrangement manually.
- Using TPUs explains how to modify
Estimatorprograms to run on a TPU.
Low Level APIs
- Introduction, which introduces the basics of how you can use TensorFlow outside of the high Level APIs.
- 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.
- Save and Restore, which explains how to save and restore variables and models.
- 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.
- TensorFlow Debugger, which explains how to use the TensorFlow debugger (tfdbg).
TensorBoard is a utility to visualize different aspects of machine learning. The following guides explain how to use TensorBoard:
- TensorBoard: Visualizing Learning, which introduces TensorBoard.
- TensorBoard: Graph Visualization, which explains how to visualize the computational graph.
- TensorBoard Histogram Dashboard which demonstrates the how to use TensorBoard's histogram dashboard.