Interface to 'TensorFlow' Datasets

Interface to 'TensorFlow' Datasets, a high-level library for building complex input pipelines from simple, re-usable pieces. See <> 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


Reference manual

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2.6.0 by Tomasz Kalinowski, a month ago

Report a bug at

Browse source code at

Authors: Tomasz Kalinowski [ctb, cph, cre] , Daniel Falbel [ctb, cph] , JJ Allaire [aut, cph] , Yuan Tang [aut] , Kevin Ushey [aut] , RStudio [cph, fnd] , Google Inc. [cph]

Documentation:   PDF Manual  

Apache License 2.0 license

Imports reticulate, tensorflow, magrittr, rlang, tidyselect, stats, generics, vctrs

Suggests testthat, knitr, keras, rsample, rmarkdown, Metrics, dplyr, tfestimators

System requirements: TensorFlow >= 1.4 (

Imported by TSPred, tfio.

Suggested by keras, kerastuneR, tfprobability.

See at CRAN