tf.contrib.data API

NOTE: The tf.contrib.data module has been deprecated. Use tf.data instead, or tf.data.experimental for the experimental transformations previously hosted in this module. We are continuing to support existing code using the tf.contrib.data APIs in the current version of TensorFlow, but will eventually remove support. The non-experimental tf.data APIs are subject to backwards compatibility guarantees.

Porting your code to tf.data

The tf.contrib.data.Dataset class has been renamed to tf.data.Dataset, and the tf.contrib.data.Iterator class has been renamed to tf.data.Iterator. Most code can be ported by removing .contrib from the names of the classes. However, there are some small differences, which are outlined below.

The arguments accepted by the Dataset.map() transformation have changed:

• dataset.map(..., num_threads=T) is now dataset.map(num_parallel_calls=T).
• dataset.map(..., output_buffer_size=B) is now dataset.map(...).prefetch(B).

Some transformations have been removed from tf.data.Dataset, and you must instead apply them using Dataset.apply() transformation. The full list of changes is as follows:

• dataset.dense_to_sparse_batch(...) is now dataset.apply(tf.data.experimental.dense_to_sparse_batch(...).
• dataset.enumerate(...) is now dataset.apply(tf.data.experimental.enumerate_dataset(...)).
• dataset.group_by_window(...) is now dataset.apply(tf.data.experimental.group_by_window(...)).
• dataset.ignore_errors() is now dataset.apply(tf.data.experimental.ignore_errors()).
• dataset.unbatch() is now dataset.apply(tf.contrib.data.unbatch()).

The Dataset.make_dataset_resource() and Iterator.dispose_op() methods have been removed from the API. Please open a GitHub issue if you have a need for either of these.