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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 keras package.

For documentation on using tfdatasets, see the package website at https://tensorflow.rstudio.com/tools/tfdatasets/.

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install.packages('tfdatasets')

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Version

2.0.0

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Apache License 2.0

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Maintainer

Daniel Falbel

Last Published

December 13th, 2019

Functions in tfdatasets (2.0.0)

dataset_shard

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

Fits a feature specification.
dataset_prefetch_to_device

A transformation that prefetches dataset values to the given device
dataset_prefetch

Creates a Dataset that prefetches elements from this dataset.
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.
dataset_map

Map a function across a dataset.
dataset_interleave

Maps map_func across this dataset, and interleaves the results
delim_record_spec

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

Combines input elements into a dataset of windows.
layer_input_from_dataset

Creates a list of inputs from a dataset
next_batch

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

Prepare a dataset for analysis
iterator_make_initializer

Create an operation that can be run to initialize this iterator
make-iterator

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

Reads CSV files into a batched dataset
scaler_standard

Creates an instance of a standard scaler
dataset_repeat

Repeats a dataset count times.
dense_features

Dense Features
feature_spec

Creates a feature specification.
dataset_shuffle

Randomly shuffles the elements of this dataset.
has_type

Identify the type of the variable.
selectors

Selectors
dataset_collect

Collects a dataset
dataset_map_and_batch

Fused implementation of dataset_map() and dataset_batch()
dataset_padded_batch

Combines consecutive elements of this dataset into padded batches
scaler

List of pre-made scalers
range_dataset

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

String-valued tensor that represents this iterator
read_files

Read a dataset from a set of files
sparse_tensor_slices_dataset

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

A dataset consisting of the results from a SQL query
step_shared_embeddings_column

Creates shared embeddings for categorical columns
dataset_take

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

Transform the dataset using the provided spec.
until_out_of_range

Execute code that traverses a dataset until an out of range condition occurs
steps

Steps for feature columns specification.
dataset_shuffle_and_repeat

Shuffles and repeats a dataset returning a new permutation for each epoch.
with_dataset

Execute code that traverses a dataset
dataset_skip

Creates a dataset that skips count elements from this dataset
iterator_get_next

Get next element from iterator
output_types

Output types and shapes
input_fn.tf_dataset

Construct a tfestimators input function from a dataset
iterator_initializer

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

Heart Disease Data Set
%>%

Pipe operator
text_line_dataset

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

Creates crosses of categorical columns
tfrecord_dataset

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

Creates a categorical column specification
scaler_min_max

Creates an instance of a min max scaler
reexports

Objects exported from other packages
step_categorical_column_with_identity

Create a categorical column with identity
step_categorical_column_with_vocabulary_file

Creates a categorical column with vocabulary file
tensor_slices_dataset

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

Samples elements at random from the datasets in datasets.
step_categorical_column_with_hash_bucket

Creates a categorical column with hash buckets specification
step_bucketized_column

Creates bucketized columns
tensors_dataset

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

Creates a step that can remove columns
step_numeric_column

Creates a numeric column specification
step_embedding_column

Creates embeddings columns
step_indicator_column

Creates Indicator Columns
zip_datasets

Creates a dataset by zipping together the given datasets.
dataset_flat_map

Maps map_func across this dataset and flattens the result.
dataset_concatenate

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

Find all nominal variables.
dataset_cache

Caches the elements in this dataset.
all_numeric

Speciy all numeric variables.
dataset_decode_delim

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

Filter a dataset by a predicate
as_tf_dataset

Add the tf_dataset class to a dataset
dataset_batch

Combines consecutive elements of this dataset into batches.