Interface to 'TensorFlow' Datasets, a high-level library for building complex input pipelines from simple, re-usable pieces. See < https://www.tensorflow.org/programmers_guide/datasets> for additional details.
The TensorFlow Dataset API provides various facilities for creating scalable input pipelines for TensorFlow models, including:
Reading data from a variety of formats including CSV files and TFRecords files (the standard binary format for TensorFlow training data).
Transforming datasets in a variety of ways including mapping arbitrary functions against them.
Shuffling, batching, and repeating datasets over a number of epochs.
Streaming interface to data for reading arbitrarily large datasets.
Reading and transforming data are TensorFlow graph operations, so are executed in C++ and in parallel with model training.
The R interface to TensorFlow datasets provides access to the Dataset API, including high-level convenience functions for easy integration with the tfestimators package.
For documentation on using tfdatasets, see the package website at https://tensorflow.rstudio.com/tools/tfdatasets/.