Dataset Input Pipeline

tf.data.Dataset allows you to build complex input pipelines. See the programmer's guide for an in-depth explanation of how to use this API.

Reader classes

Classes that create a dataset from input files.

Creating new datasets

Static methods in Dataset that create new datasets.

Transformations on existing datasets

These functions transform an existing dataset, and return a new dataset. Calls can be chained together, as shown in the example below:

train_data = train_data.batch(100).shuffle().repeat()

Custom transformation functions

Custom transformation functions can be applied to a Dataset using tf.data.Dataset.apply. Below are custom transformation functions from tf.contrib.data:

Iterating over datasets

These functions make a tf.data.Iterator from a Dataset.

The Iterator class also contains static methods that create a tf.data.Iterator that can be used with multiple Dataset objects.

Extra functions from `tf.contrib.data`