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R interface to TensorFlow Dataset API

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/.

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Install

install.packages('tfdatasets')

Monthly Downloads

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Version

1.5

License

Apache License 2.0

Issues

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0

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34

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Maintainer

JJ Allaire

Last Published

January 15th, 2018

Functions in tfdatasets (1.5)

dataset_cache

Caches the elements in this dataset.
dataset_concatenate

Creates a dataset by concatenating given dataset with this dataset.
dataset_map

Map a function across a dataset.
dataset_padded_batch

Combines consecutive elements of this dataset into padded batches
dataset_repeat

Repeats a dataset count times.
dataset_shard

Creates a dataset that includes only 1 / num_shards of this dataset.
as_tf_dataset

Add the tf_dataset class to a dataset
dataset_batch

Combines consecutive elements of this dataset into batches.
file_list_dataset

A dataset of all files matching a pattern
fixed_length_record_dataset

A dataset of fixed-length records from one or more binary files.
%>%

Pipe operator
range_dataset

Creates a dataset of a step-separated range of values.
sparse_tensor_slices_dataset

Splits each rank-N tf$SparseTensor in this dataset row-wise.
tensor_slices_dataset

Creates a dataset whose elements are slices of the given tensors.
dataset_shuffle

Randomly shuffles the elements of this dataset.
dataset_skip

Creates a dataset that skips count elements from this dataset
input_fn.tf_dataset

Construct a tfestimators input function from a dataset
iterator_get_next

Get next element from iterator
tensors_dataset

Creates a dataset with a single element, comprising the given tensors.
iterator_initializer

An operation that should be run to initialize this iterator.
iterator_make_initializer

Create an operation that can be run to initialize this iterator
tfrecord_dataset

A dataset comprising records from one or more TFRecord files.
iterator_string_handle

String-valued tensor that represents this iterator
make-iterator

Creates an iterator for enumerating the elements of this dataset.
dataset_flat_map

Maps map_func across this dataset and flattens the result.
dataset_take

Creates a dataset with at most count elements from this dataset
delim_record_spec

Specification for reading a record from a text file with delimited values
text_line_dataset

A dataset comprising lines from one or more text files.
dataset_interleave

Maps map_func across this dataset, and interleaves the results
dataset_decode_delim

Transform a dataset with delimted text lines into a dataset with named columns
dataset_filter

Filter a dataset by a predicate
dataset_prefetch

Creates a Dataset that prefetches elements from this dataset.
dataset_prepare

Prepare a dataset for analysis
next_batch

Tensor(s) for retreiving the next batch from a dataset
output_types

Output types and shapes
zip_datasets

Creates a dataset by zipping together the given datasets.
with_dataset

Execute code that traverses a dataset
read_files

Read a dataset from a set of files
reexports

Objects exported from other packages