Module: tf.data.experimental

Experimental API for building input pipelines.

This module contains experimental Dataset sources and transformations that can be used in conjunction with the tf.data.Dataset API. Note that the tf.data.experimental API is not subject to the same backwards compatibility guarantees as tf.data, but we will provide deprecation advice in advance of removing existing functionality.

See Importing Data for an overview.

Modules

service module: Experimental API for using the tf.data service.

Classes

class AutoShardPolicy: Represents the type of auto-sharding we enable.

class CheckpointInputPipelineHook: Checkpoints input pipeline state every N steps or seconds.

class CsvDataset: A Dataset comprising lines from one or more CSV files.

class DistributeOptions: Represents options for distributed data processing.

class MapVectorizationOptions: Represents options for the MapVectorization optimization.

class OptimizationOptions: Represents options for dataset optimizations.

class Optional: Represents a value that may or may not be present.

class RandomDataset: A Dataset of pseudorandom values.

class Reducer: A reducer is used for reducing a set of elements.

class SqlDataset: A Dataset consisting of the results from a SQL query.

class StatsAggregator: A stateful resource that aggregates statistics from one or more iterators.

class StatsOptions: Represents options for collecting dataset stats using StatsAggregator.

class TFRecordWriter: Writes a dataset to a TFRecord file.

class ThreadingOptions: Represents options for dataset threading.

Functions

Counter(...): Creates a Dataset that counts from start in steps of size step.

assert_cardinality(...): Asserts the cardinality of the input dataset.

bucket_by_sequence_length(...): A transformation that buckets elements in a Dataset by length.

bytes_produced_stats(...): Records the number of bytes produced by each element of the input dataset.

cardinality(...): Returns the cardinality of dataset, if known.

choose_from_datasets(...): Creates a dataset that deterministically chooses elements from datasets.

copy_to_device(...): A transformation that copies dataset elements to the given target_device.

dense_to_ragged_batch(...): A transformation that batches ragged elements into tf.RaggedTensors.

dense_to_sparse_batch(...): A transformation that batches ragged elements into tf.sparse.SparseTensors.

enumerate_dataset(...): A transformation that enumerates the elements of a dataset. (deprecated)

from_variant(...): Constructs a dataset from the given variant and structure.

get_next_as_optional(...): Returns a tf.experimental.Optional with the next element of the iterator. (deprecated)

get_single_element(...): Returns the single element in dataset as a nested structure of tensors.

get_structure(...): Returns the type signature for elements of the input dataset / iterator.

group_by_reducer(...): A transformation that groups elements and performs a reduction.

group_by_window(...): A transformation that groups windows of elements by key and reduces them.

ignore_errors(...): Creates a Dataset from another Dataset and silently ignores any errors.

latency_stats(...): Records the latency of producing each element of the input dataset.

load(...): Loads a previously saved dataset.

make_batched_features_dataset(...): Returns a Dataset of feature dictionaries from Example protos.

make_csv_dataset(...): Reads CSV files into a dataset.

make_saveable_from_iterator(...): Returns a SaveableObject for saving/restoring iterator state using Saver. (deprecated)

map_and_batch(...): Fused implementation of map and batch. (deprecated)

parallel_interleave(...): A parallel version of the Dataset.interleave() transformation. (deprecated)

parse_example_dataset(...): A transformation that parses Example protos into a dict of tensors.

prefetch_to_device(...): A transformation that prefetches dataset values to the given device.

rejection_resample(...): A transformation that resamples a dataset to achieve a target distribution.

sample_from_datasets(...): Samples elements at random from the datasets in datasets.

save(...): Saves the content of the given dataset.

scan(...): A transformation that scans a function across an input dataset.