tfds_load: Loads the named dataset into a TensorFlow Dataset

Description Usage Arguments

View source: R/load.R

Description

Loads the named dataset into a TensorFlow Dataset

Usage

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tfds_load(
  name,
  split = NULL,
  data_dir = NULL,
  batch_size = NULL,
  download = TRUE,
  shuffle_files = FALSE,
  as_supervised = FALSE,
  decoders = NULL,
  read_config = NULL,
  builder_kwargs = NULL,
  download_and_prepare_kwargs = NULL,
  as_dataset_kwargs = NULL,
  try_gcs = FALSE
)

Arguments

name

str, the registered name of the DatasetBuilder (the snake case version of the class name). This can be either "dataset_name" or "dataset_name/config_name" for datasets with BuilderConfigs. As a convenience, this string may contain comma-separated keyword arguments for the builder. For example "foo_bar/a=True,b=3" would use the FooBar dataset passing the keyword arguments a=True and b=3 (for builders with configs, it would be "foo_bar/zoo/a=True,b=3" to use the "zoo" config and pass to the builder keyword arguments a=True and b=3).

split

tfds.Split or str, which split of the data to load. If None, will return a dict with all splits (typically tfds.Split.TRAIN and tfds.Split.TEST).

data_dir

str (optional), directory to read/write data. Defaults to "~/tensorflow_datasets".

batch_size

int, if set, add a batch dimension to examples. Note that variable length features will be 0-padded. If batch_size=-1, will return the full dataset as tf.Tensors.

download

bool (optional), whether to call tfds.core.DatasetBuilder.download_and_prepare before calling tf.DatasetBuilder.as_dataset. If False, data is expected to be in data_dir. If True and the data is already in data_dir, download_and_prepare is a no-op.

shuffle_files

bool, whether to shuffle the input files. Defaults to False.

as_supervised

bool, if True, the returned tf.data.Dataset will have a 2-tuple structure (input, label) according to builder.info.supervised_keys. If False, the default, the returned tf.data.Dataset will have a dictionary with all the features.

decoders

Nested dict of Decoder objects which allow to customize the decoding. The structure should match the feature structure, but only customized feature keys need to be present. See the guide for more info.

read_config

tfds.ReadConfig, additional options to configure the input pipeline (e.g. seed, num parallel reads,...).

builder_kwargs

dict (optional), keyword arguments to be passed to the tfds.core.DatasetBuilder constructor. data_dir will be passed through by default.

download_and_prepare_kwargs

dict (optional) keyword arguments passed to tfds.core.DatasetBuilder.download_and_prepare if download=True. Allow to control where to download and extract the cached data. If not set, cache_dir and manual_dir will automatically be deduced from data_dir.

as_dataset_kwargs

dict (optional), keyword arguments passed to tfds.core.DatasetBuilder.as_dataset.

try_gcs

bool, if True, tfds.load will see if the dataset exists on the public GCS bucket before building it locally.


rstudio/tfds documentation built on May 20, 2020, 1:33 p.m.