TensorFlow Datasets

TFDS 提供一系列可立即使用的資料集，可用於 TensorFlow、Jax 和其他機器學習架構。

它會以決定性的方式處理資料的下載和準備，並建構 tf.data.Dataset (或 np.array)。

在 TensorFlow.org 上檢視

在 Google Colab 中執行

在 GitHub 上檢視原始碼

下載筆記本

安裝

TFDS 存在於兩個套件中

• pip install tensorflow-datasets：穩定版本，每隔幾個月發布一次。
• pip install tfds-nightly：每天發布，包含資料集的最新版本。

這個 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

尋找可用的資料集

所有資料集建構工具都是 tfds.core.DatasetBuilder 的子類別。若要取得可用建構工具的清單，請使用 tfds.list_builders() 或查看我們的目錄。

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',
 ...]

載入資料集

tfds.load

載入資料集最簡單的方式是 tfds.load。它會

1. 下載資料並將其儲存為 tfrecord 檔案。
2. 載入 tfrecord 並建立 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

一些常見引數

• split=：要讀取的分割 (例如 'train'、['train', 'test']、'train[80%:]' 等)。請參閱我們的分割 API 指南。
• shuffle_files=：控制是否在每個週期之間隨機排序檔案 (TFDS 將大型資料集儲存在多個較小的檔案中)。
• data_dir=：資料集儲存位置 (預設為 ~/tensorflow_datasets/)
• with_info=True：傳回包含資料集metadata的 tfds.core.DatasetInfo
• download=False：停用下載

tfds.builder

tfds.load 是 tfds.core.DatasetBuilder 的輕量包裝函式。您可以使用 tfds.core.DatasetBuilder API 取得相同的輸出

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

如果您想要產生特定的資料集，可以使用 tfds 命令列。例如

tfds build mnist

請參閱文件以瞭解可用的旗標。

逐一查看資料集

以 dict 形式

預設情況下，tf.data.Dataset 物件包含 tf.Tensor 的 dict

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.

若要找出 dict 金鑰名稱和結構，請查看我們的目錄中的資料集文件。例如：mnist 文件。

以 tuple 形式 (as_supervised=True)

透過使用 as_supervised=True，您可以針對監督式資料集取得 tuple (features, label) 取代 dict。

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.

以 numpy 形式 (tfds.as_numpy)

使用 tfds.as_numpy 進行轉換

• tf.Tensor -> np.array
• tf.data.Dataset -> Iterator[Tree[np.array]] (Tree 可以是任意巢狀 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.

以批次 tf.Tensor 形式 (batch_size=-1)

透過使用 batch_size=-1，您可以將完整資料集載入單一批次中。

這可以與 as_supervised=True 和 tfds.as_numpy 結合使用，以取得 (np.array, np.array) 形式的資料

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)

請注意，您的資料集可以放入記憶體中，且所有範例都具有相同的形狀。

為您的資料集進行基準化分析

基準化分析資料集是針對任何可迭代物件 (例如 tf.data.Dataset、tfds.as_numpy 等) 的簡單 tfds.benchmark 呼叫。

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)

• 請勿忘記使用 batch_size= kwarg 將結果依批次大小正規化。
• 在摘要中，第一個暖身批次與其他批次分開，以擷取 tf.data.Dataset 額外的設定時間 (例如緩衝區初始化等)。
• 請注意，由於 TFDS 自動快取，第二次迭代的速度快得多。
• tfds.benchmark 傳回 tfds.core.BenchmarkResult，可以檢查該結果以進行進一步分析。

建構端對端管線

若要深入瞭解，您可以查看

• 我們的端對端 Keras 範例，以查看完整的訓練管線 (包含批次處理、隨機排序等)。
• 我們的效能指南，以提升管線的速度 (秘訣：使用 tfds.benchmark(ds) 為您的資料集進行基準化分析)。

視覺化

tfds.as_dataframe

tf.data.Dataset 物件可以使用 tfds.as_dataframe 轉換為 pandas.DataFrame，以便在 Colab 上視覺化。

• 新增 tfds.core.DatasetInfo 作為 tfds.as_dataframe 的第二個引數，以視覺化圖片、音訊、文字、影片等。
• 使用 ds.take(x) 僅顯示前 x 個範例。pandas.DataFrame 會將完整資料集載入記憶體中，且顯示成本可能非常高昂。

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 傳回 matplotlib.figure.Figure (目前僅支援圖片資料集)

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.

存取資料集 metadata

所有建構工具都包含 tfds.core.DatasetInfo 物件，其中包含資料集 metadata。

可以透過以下方式存取：

• tfds.load API

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

• tfds.core.DatasetBuilder API

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

資料集資訊包含資料集的其他資訊 (版本、引用、首頁、說明等)。

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}
    }""",
)

功能 metadata (標籤名稱、圖片形狀等)

存取 tfds.features.FeatureDict

info.features

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

類別數量、標籤名稱

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

形狀、dtypes

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'>

分割 metadata (例如分割名稱、範例數量等)

存取 tfds.core.SplitDict

print(info.splits)

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

可用的分割

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

['test', 'train']

取得個別分割的資訊

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

也適用於子分割 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)]

疑難排解

手動下載 (如果下載失敗)

如果下載因某些原因失敗 (例如離線等)。您可以隨時手動下載資料，並將其放置在 manual_dir 中 (預設為 ~/tensorflow_datasets/downloads/manual/。

若要找出要下載的網址，請查看

• 適用於新的資料集 (以資料夾形式實作)：tensorflow_datasets/<type>/<dataset_name>/checksums.tsv。例如：tensorflow_datasets/datasets/bool_q/checksums.tsv。

  您可以在我們的目錄中找到資料集來源位置。

• 適用於舊的資料集：tensorflow_datasets/url_checksums/<dataset_name>.txt

修正 NonMatchingChecksumError

TFDS 透過驗證已下載網址的檢查碼來確保決定性。如果引發 NonMatchingChecksumError，可能表示

• 網站可能已關閉 (例如 503 狀態碼)。請檢查網址。
• 針對 Google Drive 網址，請稍後再試一次，因為當太多人存取相同的網址時，雲端硬碟有時會拒絕下載。請參閱錯誤
• 原始資料集檔案可能已更新。在這種情況下，應更新 TFDS 資料集建構工具。請開啟新的 Github 問題或 PR
  • 使用 tfds build --register_checksums 註冊新的檢查碼
  • 最終更新資料集產生程式碼。
  • 更新資料集 VERSION
  • 更新資料集 RELEASE_NOTES：是什麼原因導致檢查碼變更？是否有範例變更？
  • 確定資料集仍可建置。
  • 傳送 PR 給我們

引用

如果您在論文中使用 tensorflow-datasets，請加入以下引用，以及任何特定於所用資料集的引用 (可在資料集目錄中找到)。

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