# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """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](https://tensorflow.org/guide/datasets) for an overview. @@AutoShardPolicy @@AutotuneAlgorithm @@AutotuneOptions @@CheckpointInputPipelineHook @@Counter @@CsvDataset @@DatasetInitializer @@DatasetStructure @@DistributeOptions @@ExternalStatePolicy @@OptimizationOptions @@Optional @@OptionalStructure @@RaggedTensorStructure @@RandomDataset @@Reducer @@SparseTensorStructure @@SqlDataset @@Structure @@TFRecordWriter @@TensorArrayStructure @@TensorStructure @@ThreadingOptions @@assert_cardinality @@at @@bucket_by_sequence_length @@cardinality @@choose_from_datasets @@copy_to_device @@dense_to_ragged_batch @@dense_to_sparse_batch @@distribute @@enable_debug_mode @@enumerate_dataset @@from_list @@from_variant @@get_next_as_optional @@get_single_element @@get_structure @@group_by_reducer @@group_by_window @@ignore_errors @@index_table_from_dataset @@load @@make_batched_features_dataset @@make_csv_dataset @@make_saveable_from_iterator @@map_and_batch @@map_and_batch_with_legacy_function @@parallel_interleave @@parse_example_dataset @@prefetch_to_device @@rejection_resample @@sample_from_datasets @@save @@scan @@shuffle_and_repeat @@snapshot @@table_from_dataset @@take_while @@to_variant @@unbatch @@unique @@AUTOTUNE @@INFINITE_CARDINALITY @@SHARD_HINT @@UNKNOWN_CARDINALITY """ # pylint: disable=unused-import from tensorflow.python.data.experimental import service from tensorflow.python.data.experimental.ops.batching import dense_to_ragged_batch from tensorflow.python.data.experimental.ops.batching import dense_to_sparse_batch from tensorflow.python.data.experimental.ops.batching import map_and_batch from tensorflow.python.data.experimental.ops.batching import map_and_batch_with_legacy_function from tensorflow.python.data.experimental.ops.batching import unbatch from tensorflow.python.data.experimental.ops.cardinality import assert_cardinality from tensorflow.python.data.experimental.ops.cardinality import cardinality from tensorflow.python.data.experimental.ops.cardinality import INFINITE as INFINITE_CARDINALITY from tensorflow.python.data.experimental.ops.cardinality import UNKNOWN as UNKNOWN_CARDINALITY from tensorflow.python.data.experimental.ops.counter import Counter from tensorflow.python.data.experimental.ops.distribute import SHARD_HINT from tensorflow.python.data.experimental.ops.enumerate_ops import enumerate_dataset from tensorflow.python.data.experimental.ops.error_ops import ignore_errors from tensorflow.python.data.experimental.ops.from_list import from_list from tensorflow.python.data.experimental.ops.get_single_element import get_single_element from tensorflow.python.data.experimental.ops.grouping import bucket_by_sequence_length from tensorflow.python.data.experimental.ops.grouping import group_by_reducer from tensorflow.python.data.experimental.ops.grouping import group_by_window from tensorflow.python.data.experimental.ops.grouping import Reducer from tensorflow.python.data.experimental.ops.interleave_ops import choose_from_datasets from tensorflow.python.data.experimental.ops.interleave_ops import parallel_interleave from tensorflow.python.data.experimental.ops.interleave_ops import sample_from_datasets from tensorflow.python.data.experimental.ops.io import load from tensorflow.python.data.experimental.ops.io import save from tensorflow.python.data.experimental.ops.iterator_ops import CheckpointInputPipelineHook from tensorflow.python.data.experimental.ops.iterator_ops import make_saveable_from_iterator from tensorflow.python.data.experimental.ops.lookup_ops import DatasetInitializer from tensorflow.python.data.experimental.ops.lookup_ops import index_table_from_dataset from tensorflow.python.data.experimental.ops.lookup_ops import table_from_dataset from tensorflow.python.data.experimental.ops.parsing_ops import parse_example_dataset from tensorflow.python.data.experimental.ops.prefetching_ops import copy_to_device from tensorflow.python.data.experimental.ops.prefetching_ops import prefetch_to_device from tensorflow.python.data.experimental.ops.random_access import at from tensorflow.python.data.experimental.ops.random_ops import RandomDataset from tensorflow.python.data.experimental.ops.readers import CsvDataset from tensorflow.python.data.experimental.ops.readers import make_batched_features_dataset from tensorflow.python.data.experimental.ops.readers import make_csv_dataset from tensorflow.python.data.experimental.ops.readers import SqlDataset from tensorflow.python.data.experimental.ops.resampling import rejection_resample from tensorflow.python.data.experimental.ops.scan_ops import scan from tensorflow.python.data.experimental.ops.shuffle_ops import shuffle_and_repeat from tensorflow.python.data.experimental.ops.snapshot import snapshot from tensorflow.python.data.experimental.ops.take_while_ops import take_while from tensorflow.python.data.experimental.ops.unique import unique from tensorflow.python.data.experimental.ops.writers import TFRecordWriter from tensorflow.python.data.ops.dataset_ops import AUTOTUNE from tensorflow.python.data.ops.dataset_ops import DatasetSpec as DatasetStructure from tensorflow.python.data.ops.dataset_ops import from_variant from tensorflow.python.data.ops.dataset_ops import get_structure from tensorflow.python.data.ops.dataset_ops import to_variant from tensorflow.python.data.ops.debug_mode import enable_debug_mode from tensorflow.python.data.ops.iterator_ops import get_next_as_optional from tensorflow.python.data.ops.optional_ops import Optional from tensorflow.python.data.ops.optional_ops import OptionalSpec as OptionalStructure from tensorflow.python.data.ops.options import AutoShardPolicy from tensorflow.python.data.ops.options import AutotuneAlgorithm from tensorflow.python.data.ops.options import AutotuneOptions from tensorflow.python.data.ops.options import DistributeOptions from tensorflow.python.data.ops.options import ExternalStatePolicy from tensorflow.python.data.ops.options import OptimizationOptions from tensorflow.python.data.ops.options import ThreadingOptions from tensorflow.python.data.util.structure import _RaggedTensorStructure as RaggedTensorStructure from tensorflow.python.data.util.structure import _SparseTensorStructure as SparseTensorStructure from tensorflow.python.data.util.structure import _TensorArrayStructure as TensorArrayStructure from tensorflow.python.data.util.structure import _TensorStructure as TensorStructure from tensorflow.python.framework.type_spec import TypeSpec as Structure # pylint: enable=unused-import from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ "service", ] remove_undocumented(__name__, _allowed_symbols)