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TensorFlow 1 version

Experimental API for building input pipelines.

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

See Importing Data for an overview.


service module: Experimental API for using the service.


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 DatasetStructure: Type specification for

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 OptionalStructure: Type specification for tf.experimental.Optional.

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 Structure: Specifies a TensorFlow value type.

class TFRecordWriter: Writes a dataset to a TFRecord file.

class ThreadingOptions: Represents options for dataset threading.


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

RaggedTensorStructure(...): DEPRECATED FUNCTION

SparseTensorStructure(...): DEPRECATED FUNCTION

TensorArrayStructure(...): DEPRECATED FUNCTION

TensorStructure(...): DEPRECATED FUNCTION

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.

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)

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

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