TensorFlow 数据集

TFDS 提供了一组可用于 TensorFlow、Jax 和其他机器学习框架的现成数据集。

它以确定性方式处理下载和准备数据，并构建一个 tf.data.Dataset（或 np.array）。

在 TensorFlow.org 上查看

在 Google Colab 中运行

在 GitHub 上查看源代码

下载笔记本

安装

TFDS 存在于两个包中

• pip install tensorflow-datasets：稳定版本，每隔几个月发布一次。
• pip install tfds-nightly：每天发布，包含数据集的最新版本。

此 Colab 使用 tfds-nightly

pip install -q tfds-nightly tensorflow matplotlib

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf

import tensorflow_datasets as tfds

2023-10-03 09:31:42.312675: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-10-03 09:31:42.312725: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-10-03 09:31:42.312761: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

查找可用数据集

所有数据集构建器都是 tfds.core.DatasetBuilder 的子类。要获取可用构建器的列表，请使用 tfds.list_builders() 或查看我们的 目录。

tfds.list_builders()

['abstract_reasoning',
 'accentdb',
 'aeslc',
 'aflw2k3d',
 'ag_news_subset',
 'ai2_arc',
 'ai2_arc_with_ir',
 'amazon_us_reviews',
 'anli',
 'answer_equivalence',
 'arc',
 'asqa',
 'asset',
 'assin2',
 'asu_table_top_converted_externally_to_rlds',
 'austin_buds_dataset_converted_externally_to_rlds',
 'austin_sailor_dataset_converted_externally_to_rlds',
 'austin_sirius_dataset_converted_externally_to_rlds',
 'bair_robot_pushing_small',
 'bc_z',
 'bccd',
 'beans',
 'bee_dataset',
 'beir',
 'berkeley_autolab_ur5',
 'berkeley_cable_routing',
 'berkeley_fanuc_manipulation',
 'berkeley_gnm_cory_hall',
 'berkeley_gnm_recon',
 'berkeley_gnm_sac_son',
 'berkeley_mvp_converted_externally_to_rlds',
 'berkeley_rpt_converted_externally_to_rlds',
 'big_patent',
 'bigearthnet',
 'billsum',
 'binarized_mnist',
 'binary_alpha_digits',
 'ble_wind_field',
 'blimp',
 'booksum',
 'bool_q',
 'bot_adversarial_dialogue',
 'bridge',
 'bucc',
 'c4',
 'c4_wsrs',
 'caltech101',
 'caltech_birds2010',
 'caltech_birds2011',
 'cardiotox',
 'cars196',
 'cassava',
 'cats_vs_dogs',
 'celeb_a',
 'celeb_a_hq',
 'cfq',
 'cherry_blossoms',
 'chexpert',
 'cifar10',
 'cifar100',
 'cifar100_n',
 'cifar10_1',
 'cifar10_corrupted',
 'cifar10_h',
 'cifar10_n',
 'citrus_leaves',
 'cityscapes',
 'civil_comments',
 'clevr',
 'clic',
 'clinc_oos',
 'cmaterdb',
 'cmu_franka_exploration_dataset_converted_externally_to_rlds',
 'cmu_play_fusion',
 'cmu_stretch',
 'cnn_dailymail',
 'coco',
 'coco_captions',
 'coil100',
 'colorectal_histology',
 'colorectal_histology_large',
 'columbia_cairlab_pusht_real',
 'common_voice',
 'conll2002',
 'conll2003',
 'controlled_noisy_web_labels',
 'coqa',
 'corr2cause',
 'cos_e',
 'cosmos_qa',
 'covid19',
 'covid19sum',
 'crema_d',
 'criteo',
 'cs_restaurants',
 'curated_breast_imaging_ddsm',
 'cycle_gan',
 'd4rl_adroit_door',
 'd4rl_adroit_hammer',
 'd4rl_adroit_pen',
 'd4rl_adroit_relocate',
 'd4rl_antmaze',
 'd4rl_mujoco_ant',
 'd4rl_mujoco_halfcheetah',
 'd4rl_mujoco_hopper',
 'd4rl_mujoco_walker2d',
 'dart',
 'databricks_dolly',
 'davis',
 'deep1b',
 'deep_weeds',
 'definite_pronoun_resolution',
 'dementiabank',
 'diabetic_retinopathy_detection',
 'diamonds',
 'div2k',
 'dlr_edan_shared_control_converted_externally_to_rlds',
 'dlr_sara_grid_clamp_converted_externally_to_rlds',
 'dlr_sara_pour_converted_externally_to_rlds',
 'dmlab',
 'doc_nli',
 'dolphin_number_word',
 'domainnet',
 'downsampled_imagenet',
 'drop',
 'dsprites',
 'dtd',
 'duke_ultrasound',
 'e2e_cleaned',
 'efron_morris75',
 'emnist',
 'eraser_multi_rc',
 'esnli',
 'eth_agent_affordances',
 'eurosat',
 'fashion_mnist',
 'flic',
 'flores',
 'food101',
 'forest_fires',
 'fuss',
 'gap',
 'geirhos_conflict_stimuli',
 'gem',
 'genomics_ood',
 'german_credit_numeric',
 'gigaword',
 'glove100_angular',
 'glue',
 'goemotions',
 'gov_report',
 'gpt3',
 'gref',
 'groove',
 'grounded_scan',
 'gsm8k',
 'gtzan',
 'gtzan_music_speech',
 'hellaswag',
 'higgs',
 'hillstrom',
 'horses_or_humans',
 'howell',
 'i_naturalist2017',
 'i_naturalist2018',
 'i_naturalist2021',
 'iamlab_cmu_pickup_insert_converted_externally_to_rlds',
 'imagenet2012',
 'imagenet2012_corrupted',
 'imagenet2012_fewshot',
 'imagenet2012_multilabel',
 'imagenet2012_real',
 'imagenet2012_subset',
 'imagenet_a',
 'imagenet_lt',
 'imagenet_pi',
 'imagenet_r',
 'imagenet_resized',
 'imagenet_sketch',
 'imagenet_v2',
 'imagenette',
 'imagewang',
 'imdb_reviews',
 'imperialcollege_sawyer_wrist_cam',
 'irc_disentanglement',
 'iris',
 'istella',
 'jaco_play',
 'kaist_nonprehensile_converted_externally_to_rlds',
 'kddcup99',
 'kitti',
 'kmnist',
 'kuka',
 'laion400m',
 'lambada',
 'lfw',
 'librispeech',
 'librispeech_lm',
 'libritts',
 'ljspeech',
 'lm1b',
 'locomotion',
 'lost_and_found',
 'lsun',
 'lvis',
 'malaria',
 'maniskill_dataset_converted_externally_to_rlds',
 'math_dataset',
 'math_qa',
 'mctaco',
 'media_sum',
 'mlqa',
 'mnist',
 'mnist_corrupted',
 'movie_lens',
 'movie_rationales',
 'movielens',
 'moving_mnist',
 'mrqa',
 'mslr_web',
 'mt_opt',
 'mtnt',
 'multi_news',
 'multi_nli',
 'multi_nli_mismatch',
 'natural_instructions',
 'natural_questions',
 'natural_questions_open',
 'newsroom',
 'nsynth',
 'nyu_depth_v2',
 'nyu_door_opening_surprising_effectiveness',
 'nyu_franka_play_dataset_converted_externally_to_rlds',
 'nyu_rot_dataset_converted_externally_to_rlds',
 'ogbg_molpcba',
 'omniglot',
 'open_images_challenge2019_detection',
 'open_images_v4',
 'openbookqa',
 'opinion_abstracts',
 'opinosis',
 'opus',
 'oxford_flowers102',
 'oxford_iiit_pet',
 'para_crawl',
 'pass',
 'patch_camelyon',
 'paws_wiki',
 'paws_x_wiki',
 'penguins',
 'pet_finder',
 'pg19',
 'piqa',
 'places365_small',
 'placesfull',
 'plant_leaves',
 'plant_village',
 'plantae_k',
 'protein_net',
 'q_re_cc',
 'qa4mre',
 'qasc',
 'quac',
 'quality',
 'quickdraw_bitmap',
 'race',
 'radon',
 'real_toxicity_prompts',
 'reddit',
 'reddit_disentanglement',
 'reddit_tifu',
 'ref_coco',
 'resisc45',
 'rlu_atari',
 'rlu_atari_checkpoints',
 'rlu_atari_checkpoints_ordered',
 'rlu_control_suite',
 'rlu_dmlab_explore_object_rewards_few',
 'rlu_dmlab_explore_object_rewards_many',
 'rlu_dmlab_rooms_select_nonmatching_object',
 'rlu_dmlab_rooms_watermaze',
 'rlu_dmlab_seekavoid_arena01',
 'rlu_locomotion',
 'rlu_rwrl',
 'robomimic_mg',
 'robomimic_mh',
 'robomimic_ph',
 'robonet',
 'robosuite_panda_pick_place_can',
 'roboturk',
 'rock_paper_scissors',
 'rock_you',
 's3o4d',
 'salient_span_wikipedia',
 'samsum',
 'savee',
 'scan',
 'scene_parse150',
 'schema_guided_dialogue',
 'sci_tail',
 'scicite',
 'scientific_papers',
 'scrolls',
 'segment_anything',
 'sentiment140',
 'shapes3d',
 'sift1m',
 'simpte',
 'siscore',
 'smallnorb',
 'smartwatch_gestures',
 'snli',
 'so2sat',
 'speech_commands',
 'spoken_digit',
 'squad',
 'squad_question_generation',
 'stanford_dogs',
 'stanford_hydra_dataset_converted_externally_to_rlds',
 'stanford_kuka_multimodal_dataset_converted_externally_to_rlds',
 'stanford_mask_vit_converted_externally_to_rlds',
 'stanford_online_products',
 'stanford_robocook_converted_externally_to_rlds',
 'star_cfq',
 'starcraft_video',
 'stl10',
 'story_cloze',
 'summscreen',
 'sun397',
 'super_glue',
 'svhn_cropped',
 'symmetric_solids',
 'taco_play',
 'tao',
 'tatoeba',
 'ted_hrlr_translate',
 'ted_multi_translate',
 'tedlium',
 'tf_flowers',
 'the300w_lp',
 'tiny_shakespeare',
 'titanic',
 'tokyo_u_lsmo_converted_externally_to_rlds',
 'toto',
 'trec',
 'trivia_qa',
 'tydi_qa',
 'uc_merced',
 'ucf101',
 'ucsd_kitchen_dataset_converted_externally_to_rlds',
 'ucsd_pick_and_place_dataset_converted_externally_to_rlds',
 'uiuc_d3field',
 'unified_qa',
 'universal_dependencies',
 'unnatural_instructions',
 'usc_cloth_sim_converted_externally_to_rlds',
 'user_libri_audio',
 'user_libri_text',
 'utaustin_mutex',
 'utokyo_pr2_opening_fridge_converted_externally_to_rlds',
 'utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds',
 'utokyo_saytap_converted_externally_to_rlds',
 'utokyo_xarm_bimanual_converted_externally_to_rlds',
 'utokyo_xarm_pick_and_place_converted_externally_to_rlds',
 'vctk',
 'viola',
 'visual_domain_decathlon',
 'voc',
 'voxceleb',
 'voxforge',
 'waymo_open_dataset',
 'web_graph',
 'web_nlg',
 'web_questions',
 'webvid',
 'wider_face',
 'wiki40b',
 'wiki_auto',
 'wiki_bio',
 'wiki_dialog',
 'wiki_table_questions',
 'wiki_table_text',
 'wikiann',
 'wikihow',
 'wikipedia',
 'wikipedia_toxicity_subtypes',
 'wine_quality',
 'winogrande',
 'wit',
 'wit_kaggle',
 'wmt13_translate',
 'wmt14_translate',
 'wmt15_translate',
 'wmt16_translate',
 'wmt17_translate',
 'wmt18_translate',
 'wmt19_translate',
 'wmt_t2t_translate',
 'wmt_translate',
 'wordnet',
 'wsc273',
 'xnli',
 'xquad',
 'xsum',
 'xtreme_pawsx',
 'xtreme_pos',
 'xtreme_s',
 'xtreme_xnli',
 'yahoo_ltrc',
 'yelp_polarity_reviews',
 'yes_no',
 'youtube_vis',
 'huggingface:acronym_identification',
 'huggingface:ade_corpus_v2',
 'huggingface:adv_glue',
 'huggingface:adversarial_qa',
 'huggingface:aeslc',
 'huggingface:afrikaans_ner_corpus',
 'huggingface:ag_news',
 'huggingface:ai2_arc',
 'huggingface:air_dialogue',
 'huggingface:ajgt_twitter_ar',
 'huggingface:allegro_reviews',
 'huggingface:allocine',
 'huggingface:alt',
 'huggingface:amazon_polarity',
 'huggingface:amazon_reviews_multi',
 'huggingface:amazon_us_reviews',
 'huggingface:ambig_qa',
 'huggingface:americas_nli',
 'huggingface:ami',
 'huggingface:amttl',
 'huggingface:anli',
 'huggingface:app_reviews',
 'huggingface:aqua_rat',
 'huggingface:aquamuse',
 'huggingface:ar_cov19',
 'huggingface:ar_res_reviews',
 'huggingface:ar_sarcasm',
 'huggingface:arabic_billion_words',
 'huggingface:arabic_pos_dialect',
 'huggingface:arabic_speech_corpus',
 'huggingface:arcd',
 'huggingface:arsentd_lev',
 'huggingface:art',
 'huggingface:arxiv_dataset',
 'huggingface:ascent_kb',
 'huggingface:aslg_pc12',
 'huggingface:asnq',
 'huggingface:asset',
 'huggingface:assin',
 'huggingface:assin2',
 'huggingface:atomic',
 'huggingface:autshumato',
 'huggingface:babi_qa',
 'huggingface:banking77',
 'huggingface:bbaw_egyptian',
 'huggingface:bbc_hindi_nli',
 'huggingface:bc2gm_corpus',
 'huggingface:beans',
 'huggingface:best2009',
 'huggingface:bianet',
 'huggingface:bible_para',
 'huggingface:big_patent',
 'huggingface:bigbench',
 'huggingface:billsum',
 'huggingface:bing_coronavirus_query_set',
 'huggingface:biomrc',
 'huggingface:biosses',
 'huggingface:biwi_kinect_head_pose',
 'huggingface:blbooks',
 'huggingface:blbooksgenre',
 'huggingface:blended_skill_talk',
 'huggingface:blimp',
 'huggingface:blog_authorship_corpus',
 'huggingface:bn_hate_speech',
 'huggingface:bnl_newspapers',
 'huggingface:bookcorpus',
 'huggingface:bookcorpusopen',
 'huggingface:boolq',
 'huggingface:bprec',
 'huggingface:break_data',
 'huggingface:brwac',
 'huggingface:bsd_ja_en',
 'huggingface:bswac',
 'huggingface:c3',
 'huggingface:c4',
 'huggingface:cail2018',
 'huggingface:caner',
 'huggingface:capes',
 'huggingface:casino',
 'huggingface:catalonia_independence',
 'huggingface:cats_vs_dogs',
 'huggingface:cawac',
 'huggingface:cbt',
 'huggingface:cc100',
 'huggingface:cc_news',
 'huggingface:ccaligned_multilingual',
 'huggingface:cdsc',
 'huggingface:cdt',
 'huggingface:cedr',
 'huggingface:cfq',
 'huggingface:chr_en',
 'huggingface:cifar10',
 'huggingface:cifar100',
 'huggingface:circa',
 'huggingface:civil_comments',
 'huggingface:clickbait_news_bg',
 'huggingface:climate_fever',
 'huggingface:clinc_oos',
 'huggingface:clue',
 'huggingface:cmrc2018',
 'huggingface:cmu_hinglish_dog',
 'huggingface:cnn_dailymail',
 'huggingface:coached_conv_pref',
 'huggingface:coarse_discourse',
 'huggingface:codah',
 'huggingface:code_search_net',
 'huggingface:code_x_glue_cc_clone_detection_big_clone_bench',
 'huggingface:code_x_glue_cc_clone_detection_poj104',
 'huggingface:code_x_glue_cc_cloze_testing_all',
 'huggingface:code_x_glue_cc_cloze_testing_maxmin',
 'huggingface:code_x_glue_cc_code_completion_line',
 'huggingface:code_x_glue_cc_code_completion_token',
 'huggingface:code_x_glue_cc_code_refinement',
 'huggingface:code_x_glue_cc_code_to_code_trans',
 'huggingface:code_x_glue_cc_defect_detection',
 'huggingface:code_x_glue_ct_code_to_text',
 'huggingface:code_x_glue_tc_nl_code_search_adv',
 'huggingface:code_x_glue_tc_text_to_code',
 'huggingface:code_x_glue_tt_text_to_text',
 'huggingface:com_qa',
 'huggingface:common_gen',
 'huggingface:common_language',
 'huggingface:common_voice',
 'huggingface:commonsense_qa',
 'huggingface:competition_math',
 'huggingface:compguesswhat',
 'huggingface:conceptnet5',
 'huggingface:conceptual_12m',
 'huggingface:conceptual_captions',
 'huggingface:conll2000',
 'huggingface:conll2002',
 'huggingface:conll2003',
 'huggingface:conll2012_ontonotesv5',
 'huggingface:conllpp',
 'huggingface:consumer-finance-complaints',
 'huggingface:conv_ai',
 'huggingface:conv_ai_2',
 'huggingface:conv_ai_3',
 'huggingface:conv_questions',
 'huggingface:coqa',
 'huggingface:cord19',
 'huggingface:cornell_movie_dialog',
 'huggingface:cos_e',
 'huggingface:cosmos_qa',
 'huggingface:counter',
 'huggingface:covid_qa_castorini',
 'huggingface:covid_qa_deepset',
 'huggingface:covid_qa_ucsd',
 'huggingface:covid_tweets_japanese',
 'huggingface:covost2',
 'huggingface:cppe-5',
 'huggingface:craigslist_bargains',
 'huggingface:crawl_domain',
 'huggingface:crd3',
 'huggingface:crime_and_punish',
 'huggingface:crows_pairs',
 'huggingface:cryptonite',
 'huggingface:cs_restaurants',
 'huggingface:cuad',
 'huggingface:curiosity_dialogs',
 'huggingface:daily_dialog',
 'huggingface:dane',
 'huggingface:danish_political_comments',
 'huggingface:dart',
 'huggingface:datacommons_factcheck',
 'huggingface:dbpedia_14',
 'huggingface:dbrd',
 'huggingface:deal_or_no_dialog',
 'huggingface:definite_pronoun_resolution',
 'huggingface:dengue_filipino',
 'huggingface:dialog_re',
 'huggingface:diplomacy_detection',
 'huggingface:disaster_response_messages',
 'huggingface:discofuse',
 'huggingface:discovery',
 'huggingface:disfl_qa',
 'huggingface:doc2dial',
 'huggingface:docred',
 'huggingface:doqa',
 'huggingface:dream',
 'huggingface:drop',
 'huggingface:duorc',
 'huggingface:dutch_social',
 'huggingface:dyk',
 'huggingface:e2e_nlg',
 'huggingface:e2e_nlg_cleaned',
 'huggingface:ecb',
 'huggingface:ecthr_cases',
 'huggingface:eduge',
 'huggingface:ehealth_kd',
 'huggingface:eitb_parcc',
 'huggingface:electricity_load_diagrams',
 'huggingface:eli5',
 'huggingface:eli5_category',
 'huggingface:elkarhizketak',
 'huggingface:emea',
 'huggingface:emo',
 'huggingface:emotion',
 'huggingface:emotone_ar',
 'huggingface:empathetic_dialogues',
 'huggingface:enriched_web_nlg',
 'huggingface:enwik8',
 'huggingface:eraser_multi_rc',
 'huggingface:esnli',
 'huggingface:eth_py150_open',
 'huggingface:ethos',
 'huggingface:ett',
 'huggingface:eu_regulatory_ir',
 'huggingface:eurlex',
 'huggingface:euronews',
 'huggingface:europa_eac_tm',
 'huggingface:europa_ecdc_tm',
 'huggingface:europarl_bilingual',
 'huggingface:event2Mind',
 'huggingface:evidence_infer_treatment',
 'huggingface:exams',
 'huggingface:factckbr',
 'huggingface:fake_news_english',
 'huggingface:fake_news_filipino',
 'huggingface:farsi_news',
 'huggingface:fashion_mnist',
 'huggingface:fever',
 'huggingface:few_rel',
 'huggingface:financial_phrasebank',
 'huggingface:finer',
 'huggingface:flores',
 'huggingface:flue',
 'huggingface:food101',
 'huggingface:fquad',
 'huggingface:freebase_qa',
 'huggingface:gap',
 'huggingface:gem',
 'huggingface:generated_reviews_enth',
 'huggingface:generics_kb',
 'huggingface:german_legal_entity_recognition',
 'huggingface:germaner',
 'huggingface:germeval_14',
 'huggingface:giga_fren',
 'huggingface:gigaword',
 'huggingface:glucose',
 'huggingface:glue',
 'huggingface:gnad10',
 'huggingface:go_emotions',
 'huggingface:gooaq',
 'huggingface:google_wellformed_query',
 'huggingface:grail_qa',
 'huggingface:great_code',
 'huggingface:greek_legal_code',
 'huggingface:gsm8k',
 'huggingface:guardian_authorship',
 'huggingface:gutenberg_time',
 'huggingface:hans',
 'huggingface:hansards',
 'huggingface:hard',
 'huggingface:harem',
 'huggingface:has_part',
 'huggingface:hate_offensive',
 'huggingface:hate_speech18',
 'huggingface:hate_speech_filipino',
 'huggingface:hate_speech_offensive',
 'huggingface:hate_speech_pl',
 'huggingface:hate_speech_portuguese',
 'huggingface:hatexplain',
 'huggingface:hausa_voa_ner',
 'huggingface:hausa_voa_topics',
 'huggingface:hda_nli_hindi',
 'huggingface:head_qa',
 'huggingface:health_fact',
 'huggingface:hebrew_projectbenyehuda',
 'huggingface:hebrew_sentiment',
 'huggingface:hebrew_this_world',
 'huggingface:hellaswag',
 'huggingface:hendrycks_test',
 'huggingface:hind_encorp',
 'huggingface:hindi_discourse',
 'huggingface:hippocorpus',
 'huggingface:hkcancor',
 'huggingface:hlgd',
 'huggingface:hope_edi',
 'huggingface:hotpot_qa',
 'huggingface:hover',
 'huggingface:hrenwac_para',
 'huggingface:hrwac',
 'huggingface:humicroedit',
 'huggingface:hybrid_qa',
 'huggingface:hyperpartisan_news_detection',
 'huggingface:iapp_wiki_qa_squad',
 'huggingface:id_clickbait',
 'huggingface:id_liputan6',
 'huggingface:id_nergrit_corpus',
 'huggingface:id_newspapers_2018',
 'huggingface:id_panl_bppt',
 'huggingface:id_puisi',
 'huggingface:igbo_english_machine_translation',
 'huggingface:igbo_monolingual',
 'huggingface:igbo_ner',
 'huggingface:ilist',
 'huggingface:imagenet-1k',
 'huggingface:imagenet_sketch',
 'huggingface:imdb',
 'huggingface:imdb_urdu_reviews',
 'huggingface:imppres',
 'huggingface:indic_glue',
 'huggingface:indonli',
 'huggingface:indonlu',
 'huggingface:inquisitive_qg',
 'huggingface:interpress_news_category_tr',
 'huggingface:interpress_news_category_tr_lite',
 'huggingface:irc_disentangle',
 'huggingface:isixhosa_ner_corpus',
 'huggingface:isizulu_ner_corpus',
 'huggingface:iwslt2017',
 'huggingface:jeopardy',
 'huggingface:jfleg',
 'huggingface:jigsaw_toxicity_pred',
 'huggingface:jigsaw_unintended_bias',
 'huggingface:jnlpba',
 'huggingface:journalists_questions',
 'huggingface:kan_hope',
 'huggingface:kannada_news',
 'huggingface:kd_conv',
 'huggingface:kde4',
 'huggingface:kelm',
 'huggingface:kilt_tasks',
 'huggingface:kilt_wikipedia',
 'huggingface:kinnews_kirnews',
 'huggingface:klue',
 'huggingface:kor_3i4k',
 'huggingface:kor_hate',
 'huggingface:kor_ner',
 'huggingface:kor_nli',
 'huggingface:kor_nlu',
 'huggingface:kor_qpair',
 'huggingface:kor_sae',
 'huggingface:kor_sarcasm',
 'huggingface:labr',
 'huggingface:lama',
 'huggingface:lambada',
 'huggingface:large_spanish_corpus',
 'huggingface:laroseda',
 'huggingface:lc_quad',
 'huggingface:lccc',
 'huggingface:lener_br',
 'huggingface:lex_glue',
 'huggingface:liar',
 'huggingface:librispeech_asr',
 'huggingface:librispeech_lm',
 'huggingface:limit',
 'huggingface:lince',
 'huggingface:linnaeus',
 'huggingface:liveqa',
 'huggingface:lj_speech',
 'huggingface:lm1b',
 'huggingface:lst20',
 'huggingface:m_lama',
 'huggingface:mac_morpho',
 'huggingface:makhzan',
 'huggingface:masakhaner',
 'huggingface:math_dataset',
 'huggingface:math_qa',
 'huggingface:matinf',
 'huggingface:mbpp',
 'huggingface:mc4',
 'huggingface:mc_taco',
 'huggingface:md_gender_bias',
 'huggingface:mdd',
 'huggingface:med_hop',
 'huggingface:medal',
 'huggingface:medical_dialog',
 'huggingface:medical_questions_pairs',
 'huggingface:medmcqa',
 'huggingface:menyo20k_mt',
 'huggingface:meta_woz',
 'huggingface:metashift',
 'huggingface:metooma',
 'huggingface:metrec',
 'huggingface:miam',
 'huggingface:mkb',
 'huggingface:mkqa',
 'huggingface:mlqa',
 'huggingface:mlsum',
 'huggingface:mnist',
 'huggingface:mocha',
 'huggingface:monash_tsf',
 'huggingface:moroco',
 'huggingface:movie_rationales',
 'huggingface:mrqa',
 'huggingface:ms_marco',
 'huggingface:ms_terms',
 'huggingface:msr_genomics_kbcomp',
 'huggingface:msr_sqa',
 'huggingface:msr_text_compression',
 'huggingface:msr_zhen_translation_parity',
 'huggingface:msra_ner',
 'huggingface:mt_eng_vietnamese',
 'huggingface:muchocine',
 'huggingface:multi_booked',
 'huggingface:multi_eurlex',
 'huggingface:multi_news',
 'huggingface:multi_nli',
 'huggingface:multi_nli_mismatch',
 'huggingface:multi_para_crawl',
 'huggingface:multi_re_qa',
 'huggingface:multi_woz_v22',
 'huggingface:multi_x_science_sum',
 'huggingface:multidoc2dial',
 'huggingface:multilingual_librispeech',
 'huggingface:mutual_friends',
 'huggingface:mwsc',
 'huggingface:myanmar_news',
 'huggingface:narrativeqa',
 'huggingface:narrativeqa_manual',
 'huggingface:natural_questions',
 'huggingface:ncbi_disease',
 'huggingface:nchlt',
 'huggingface:ncslgr',
 'huggingface:nell',
 'huggingface:neural_code_search',
 'huggingface:news_commentary',
 'huggingface:newsgroup',
 'huggingface:newsph',
 'huggingface:newsph_nli',
 'huggingface:newspop',
 'huggingface:newsqa',
 'huggingface:newsroom',
 'huggingface:nkjp-ner',
 'huggingface:nli_tr',
 'huggingface:nlu_evaluation_data',
 'huggingface:norec',
 'huggingface:norne',
 'huggingface:norwegian_ner',
 'huggingface:nq_open',
 'huggingface:nsmc',
 'huggingface:numer_sense',
 'huggingface:numeric_fused_head',
 'huggingface:oclar',
 'huggingface:offcombr',
 'huggingface:offenseval2020_tr',
 'huggingface:offenseval_dravidian',
 'huggingface:ofis_publik',
 'huggingface:ohsumed',
 'huggingface:ollie',
 'huggingface:omp',
 'huggingface:onestop_english',
 'huggingface:onestop_qa',
 'huggingface:open_subtitles',
 'huggingface:openai_humaneval',
 'huggingface:openbookqa',
 'huggingface:openslr',
 'huggingface:openwebtext',
 'huggingface:opinosis',
 'huggingface:opus100',
 'huggingface:opus_books',
 'huggingface:opus_dgt',
 'huggingface:opus_dogc',
 'huggingface:opus_elhuyar',
 'huggingface:opus_euconst',
 'huggingface:opus_finlex',
 'huggingface:opus_fiskmo',
 'huggingface:opus_gnome',
 'huggingface:opus_infopankki',
 'huggingface:opus_memat',
 'huggingface:opus_montenegrinsubs',
 'huggingface:opus_openoffice',
 'huggingface:opus_paracrawl',
 'huggingface:opus_rf',
 'huggingface:opus_tedtalks',
 'huggingface:opus_ubuntu',
 'huggingface:opus_wikipedia',
 'huggingface:opus_xhosanavy',
 'huggingface:orange_sum',
 'huggingface:oscar',
 'huggingface:para_crawl',
 'huggingface:para_pat',
 'huggingface:parsinlu_reading_comprehension',
 'huggingface:pass',
 'huggingface:paws',
 'huggingface:paws-x',
 'huggingface:pec',
 'huggingface:peer_read',
 'huggingface:peoples_daily_ner',
 'huggingface:per_sent',
 'huggingface:persian_ner',
 'huggingface:pg19',
 'huggingface:php',
 'huggingface:piaf',
 'huggingface:pib',
 'huggingface:piqa',
 'huggingface:pn_summary',
 'huggingface:poem_sentiment',
 'huggingface:polemo2',
 'huggingface:poleval2019_cyberbullying',
 'huggingface:poleval2019_mt',
 'huggingface:polsum',
 'huggingface:polyglot_ner',
 'huggingface:prachathai67k',
 'huggingface:pragmeval',
 'huggingface:proto_qa',
 'huggingface:psc',
 'huggingface:ptb_text_only',
 'huggingface:pubmed',
 'huggingface:pubmed_qa',
 'huggingface:py_ast',
 'huggingface:qa4mre',
 'huggingface:qa_srl',
 'huggingface:qa_zre',
 'huggingface:qangaroo',
 'huggingface:qanta',
 'huggingface:qasc',
 'huggingface:qasper',
 'huggingface:qed',
 'huggingface:qed_amara',
 'huggingface:quac',
 'huggingface:quail',
 'huggingface:quarel',
 'huggingface:quartz',
 'huggingface:quickdraw',
 'huggingface:quora',
 'huggingface:quoref',
 'huggingface:race',
 'huggingface:re_dial',
 'huggingface:reasoning_bg',
 'huggingface:recipe_nlg',
 'huggingface:reclor',
 'huggingface:red_caps',
 'huggingface:reddit',
 'huggingface:reddit_tifu',
 'huggingface:refresd',
 'huggingface:reuters21578',
 'huggingface:riddle_sense',
 'huggingface:ro_sent',
 'huggingface:ro_sts',
 'huggingface:ro_sts_parallel',
 'huggingface:roman_urdu',
 'huggingface:roman_urdu_hate_speech',
 'huggingface:ronec',
 'huggingface:ropes',
 'huggingface:rotten_tomatoes',
 'huggingface:russian_super_glue',
 'huggingface:rvl_cdip',
 'huggingface:s2orc',
 'huggingface:samsum',
 'huggingface:sanskrit_classic',
 'huggingface:saudinewsnet',
 'huggingface:sberquad',
 'huggingface:sbu_captions',
 'huggingface:scan',
 'huggingface:scb_mt_enth_2020',
 'huggingface:scene_parse_150',
 'huggingface:schema_guided_dstc8',
 'huggingface:scicite',
 'huggingface:scielo',
 'huggingface:scientific_papers',
 'huggingface:scifact',
 'huggingface:sciq',
 'huggingface:scitail',
 'huggingface:scitldr',
 'huggingface:search_qa',
 'huggingface:sede',
 'huggingface:selqa',
 'huggingface:sem_eval_2010_task_8',
 'huggingface:sem_eval_2014_task_1',
 'huggingface:sem_eval_2018_task_1',
 'huggingface:sem_eval_2020_task_11',
 'huggingface:sent_comp',
 'huggingface:senti_lex',
 'huggingface:senti_ws',
 'huggingface:sentiment140',
 'huggingface:sepedi_ner',
 'huggingface:sesotho_ner_corpus',
 'huggingface:setimes',
 'huggingface:setswana_ner_corpus',
 'huggingface:sharc',
 'huggingface:sharc_modified',
 'huggingface:sick',
 'huggingface:silicone',
 'huggingface:simple_questions_v2',
 'huggingface:siswati_ner_corpus',
 'huggingface:smartdata',
 'huggingface:sms_spam',
 'huggingface:snips_built_in_intents',
 'huggingface:snli',
 'huggingface:snow_simplified_japanese_corpus',
 'huggingface:so_stacksample',
 'huggingface:social_bias_frames',
 'huggingface:social_i_qa',
 'huggingface:sofc_materials_articles',
 'huggingface:sogou_news',
 ...]

加载数据集

tfds.load

加载数据集的最简单方法是 tfds.load。它将

1. 下载数据并将其保存为 tfrecord 文件。
2. 加载 tfrecord 并创建 tf.data.Dataset。

ds = tfds.load('mnist', split='train', shuffle_files=True)
assert isinstance(ds, tf.data.Dataset)
print(ds)

<_PrefetchDataset element_spec={'image': TensorSpec(shape=(28, 28, 1), dtype=tf.uint8, name=None), 'label': TensorSpec(shape=(), dtype=tf.int64, name=None)}>
2023-10-03 09:31:47.319649: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected

一些常用参数

• split=：要读取的拆分（例如 'train'、['train', 'test']、'train[80%:]' 等）。请参阅我们的 拆分 API 指南。
• shuffle_files=：控制是否在每个 epoch 之间对文件进行混洗（TFDS 将大型数据集存储在多个较小的文件中）。
• data_dir=：保存数据集的位置（默认为 ~/tensorflow_datasets/）
• with_info=True：返回包含数据集元数据的 tfds.core.DatasetInfo
• download=False：禁用下载

tfds.builder

tfds.load 是 tfds.core.DatasetBuilder 的一个薄包装器。您可以使用 tfds.core.DatasetBuilder API 获得相同的输出

builder = tfds.builder('mnist')
# 1. Create the tfrecord files (no-op if already exists)
builder.download_and_prepare()
# 2. Load the `tf.data.Dataset`
ds = builder.as_dataset(split='train', shuffle_files=True)
print(ds)

<_PrefetchDataset element_spec={'image': TensorSpec(shape=(28, 28, 1), dtype=tf.uint8, name=None), 'label': TensorSpec(shape=(), dtype=tf.int64, name=None)}>

tfds build CLI

如果您想生成特定数据集，可以使用 tfds 命令行。例如

tfds build mnist

请参阅 文档 以了解可用标志。

遍历数据集

作为字典

默认情况下，tf.data.Dataset 对象包含一个 dict，其中包含 tf.Tensor。

ds = tfds.load('mnist', split='train')
ds = ds.take(1)  # Only take a single example

for example in ds:  # example is `{'image': tf.Tensor, 'label': tf.Tensor}`
  print(list(example.keys()))
  image = example["image"]
  label = example["label"]
  print(image.shape, label)

['image', 'label']
(28, 28, 1) tf.Tensor(4, shape=(), dtype=int64)
2023-10-03 09:31:49.152950: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

要了解 dict 键名和结构，请查看 我们的目录 中的数据集文档。例如：mnist 文档。

作为元组 (as_supervised=True)

通过使用 as_supervised=True，您可以获得一个元组 (features, label)，而不是用于监督数据集的元组。

ds = tfds.load('mnist', split='train', as_supervised=True)
ds = ds.take(1)

for image, label in ds:  # example is (image, label)
  print(image.shape, label)

(28, 28, 1) tf.Tensor(4, shape=(), dtype=int64)
2023-10-03 09:31:50.061634: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

作为 numpy (tfds.as_numpy)

使用 tfds.as_numpy 转换

• tf.Tensor -> np.array
• tf.data.Dataset -> Iterator[Tree[np.array]] (Tree 可以是任意嵌套的 Dict, Tuple)

ds = tfds.load('mnist', split='train', as_supervised=True)
ds = ds.take(1)

for image, label in tfds.as_numpy(ds):
  print(type(image), type(label), label)

<class 'numpy.ndarray'> <class 'numpy.int64'> 4
2023-10-03 09:31:51.019114: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

作为批处理的 tf.Tensor (batch_size=-1)

通过使用 batch_size=-1，您可以将整个数据集加载到一个批次中。

这可以与 as_supervised=True 和 tfds.as_numpy 结合使用，以 (np.array, np.array) 的形式获取数据。

image, label = tfds.as_numpy(tfds.load(
    'mnist',
    split='test',
    batch_size=-1,
    as_supervised=True,
))

print(type(image), image.shape)

<class 'numpy.ndarray'> (10000, 28, 28, 1)

请注意，您的数据集必须能够放入内存，并且所有示例都必须具有相同的形状。

对您的数据集进行基准测试

对数据集进行基准测试是一个简单的 tfds.benchmark 调用，适用于任何可迭代对象（例如 tf.data.Dataset，tfds.as_numpy，...）。

ds = tfds.load('mnist', split='train')
ds = ds.batch(32).prefetch(1)

tfds.benchmark(ds, batch_size=32)
tfds.benchmark(ds, batch_size=32)  # Second epoch much faster due to auto-caching

************ Summary ************

Examples/sec (First included) 47739.04 ex/sec (total: 60032 ex, 1.26 sec)
Examples/sec (First only) 99.23 ex/sec (total: 32 ex, 0.32 sec)
Examples/sec (First excluded) 64169.04 ex/sec (total: 60000 ex, 0.94 sec)

************ Summary ************

Examples/sec (First included) 187387.35 ex/sec (total: 60032 ex, 0.32 sec)
Examples/sec (First only) 2385.47 ex/sec (total: 32 ex, 0.01 sec)
Examples/sec (First excluded) 195472.46 ex/sec (total: 60000 ex, 0.31 sec)

• 不要忘记使用 batch_size= 关键字参数对每个批次大小的结果进行归一化。
• 在摘要中，第一个预热批次与其他批次分开，以捕获 tf.data.Dataset 的额外设置时间（例如缓冲区初始化，...）。
• 请注意，由于 TFDS 自动缓存，第二次迭代的速度要快得多。
• tfds.benchmark 返回一个 tfds.core.BenchmarkResult，可以对其进行检查以进行进一步分析。

构建端到端管道

要更深入地了解，您可以查看

• 我们的 端到端 Keras 示例，以了解完整的训练管道（包括批处理、混洗，...）。
• 我们的 性能指南，以提高您管道的速度（提示：使用 tfds.benchmark(ds) 对您的数据集进行基准测试）。

可视化

tfds.as_dataframe

tf.data.Dataset 对象可以使用 pandas.DataFrame 和 tfds.as_dataframe 转换为 Colab 上的可视化形式。

• 将 tfds.core.DatasetInfo 作为 tfds.as_dataframe 的第二个参数，以可视化图像、音频、文本、视频，...。
• 使用 ds.take(x) 只显示前 x 个示例。 pandas.DataFrame 会将整个数据集加载到内存中，这对于显示来说可能非常昂贵。

ds, info = tfds.load('mnist', split='train', with_info=True)

tfds.as_dataframe(ds.take(4), info)

2023-10-03 09:31:54.816533: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

tfds.show_examples

tfds.show_examples 返回一个 matplotlib.figure.Figure（目前仅支持图像数据集）。

ds, info = tfds.load('mnist', split='train', with_info=True)

fig = tfds.show_examples(ds, info)

2023-10-03 09:31:55.822856: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

访问数据集元数据

所有构建器都包含一个 tfds.core.DatasetInfo 对象，其中包含数据集元数据。

可以通过以下方式访问它

• The tfds.load API

ds, info = tfds.load('mnist', with_info=True)

• The tfds.core.DatasetBuilder API

builder = tfds.builder('mnist')
info = builder.info

数据集信息包含有关数据集的附加信息（版本、引用、主页、描述，...）。

print(info)

tfds.core.DatasetInfo(
    name='mnist',
    full_name='mnist/3.0.1',
    description="""
    The MNIST database of handwritten digits.
    """,
    homepage='http://yann.lecun.com/exdb/mnist/',
    data_dir='gs://tensorflow-datasets/datasets/mnist/3.0.1',
    file_format=tfrecord,
    download_size=11.06 MiB,
    dataset_size=21.00 MiB,
    features=FeaturesDict({
        'image': Image(shape=(28, 28, 1), dtype=uint8),
        'label': ClassLabel(shape=(), dtype=int64, num_classes=10),
    }),
    supervised_keys=('image', 'label'),
    disable_shuffling=False,
    splits={
        'test': <SplitInfo num_examples=10000, num_shards=1>,
        'train': <SplitInfo num_examples=60000, num_shards=1>,
    },
    citation="""@article{lecun2010mnist,
      title={MNIST handwritten digit database},
      author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
      journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
      volume={2},
      year={2010}
    }""",
)

特征元数据（标签名称、图像形状，...）

访问 tfds.features.FeatureDict

info.features

FeaturesDict({
    'image': Image(shape=(28, 28, 1), dtype=uint8),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=10),
})

类别数量、标签名称

print(info.features["label"].num_classes)
print(info.features["label"].names)
print(info.features["label"].int2str(7))  # Human readable version (8 -> 'cat')
print(info.features["label"].str2int('7'))

10
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
7
7

形状、数据类型

print(info.features.shape)
print(info.features.dtype)
print(info.features['image'].shape)
print(info.features['image'].dtype)

WARNING:absl:`FeatureConnector.dtype` is deprecated. Please change your code to use NumPy with the field `FeatureConnector.np_dtype` or use TensorFlow with the field `FeatureConnector.tf_dtype`.
WARNING:absl:`FeatureConnector.dtype` is deprecated. Please change your code to use NumPy with the field `FeatureConnector.np_dtype` or use TensorFlow with the field `FeatureConnector.tf_dtype`.
{'image': (28, 28, 1), 'label': ()}
{'image': tf.uint8, 'label': tf.int64}
(28, 28, 1)
<dtype: 'uint8'>

拆分元数据（例如拆分名称、示例数量，...）

访问 tfds.core.SplitDict

print(info.splits)

{'test': <SplitInfo num_examples=10000, num_shards=1>, 'train': <SplitInfo num_examples=60000, num_shards=1>}

可用拆分

print(list(info.splits.keys()))

['test', 'train']

获取有关单个拆分的信息

print(info.splits['train'].num_examples)
print(info.splits['train'].filenames)
print(info.splits['train'].num_shards)

60000
['mnist-train.tfrecord-00000-of-00001']
1

它也适用于子拆分 API

print(info.splits['train[15%:75%]'].num_examples)
print(info.splits['train[15%:75%]'].file_instructions)

36000
[FileInstruction(filename='gs://tensorflow-datasets/datasets/mnist/3.0.1/mnist-train.tfrecord-00000-of-00001', skip=9000, take=36000, examples_in_shard=60000)]

故障排除

手动下载（如果下载失败）

如果下载由于某些原因失败（例如离线，...），您可以始终手动下载数据并将其放置在 manual_dir 中（默认值为 ~/tensorflow_datasets/downloads/manual/）。

要了解要下载哪些 URL，请查看

• 对于新数据集（作为文件夹实现）：tensorflow_datasets/<type>/<dataset_name>/checksums.tsv。例如：tensorflow_datasets/datasets/bool_q/checksums.tsv.

  您可以在 我们的目录 中找到数据集源位置。

• 对于旧数据集：tensorflow_datasets/url_checksums/<dataset_name>.txt

修复 NonMatchingChecksumError

TFDS 通过验证下载 URL 的校验和来确保确定性。如果出现 NonMatchingChecksumError，可能表示

• 网站可能已关闭（例如 503 状态代码）。请检查 URL。
• 对于 Google Drive URL，请稍后再试，因为 Drive 有时会拒绝下载，因为太多人访问同一个 URL。请参阅 错误
• 原始数据集文件可能已更新。在这种情况下，应更新 TFDS 数据集构建器。请打开一个新的 Github 问题或 PR
  • 使用 tfds build --register_checksums 注册新的校验和
  • 最终更新数据集生成代码。
  • 更新数据集 VERSION
  • 更新数据集 RELEASE_NOTES：是什么导致校验和发生变化？是否有示例发生变化？
  • 确保数据集仍然可以构建。
  • 向我们发送 PR

引用

如果您在论文中使用 tensorflow-datasets，请除了任何特定于所用数据集的引用（可以在 数据集目录 中找到）之外，还包括以下引用。

@misc{TFDS,
  title = { {TensorFlow Datasets}, A collection of ready-to-use datasets},
  howpublished = {\url{https://tensorflowcn.cn/datasets} },
}