Kumpulan Data TensorFlow

TFDS menyediakan kumpulan set data siap pakai untuk digunakan dengan TensorFlow, Jax, dan framework Machine Learning lainnya.

Ini menangani pengunduhan dan persiapan data secara deterministik dan pembuatan tf.data.Dataset (atau np.array ).

Lihat di TensorFlow.org

Jalankan di Google Colab

Lihat sumber di GitHub

Unduh buku catatan

Instalasi

TFDS ada dalam dua paket:

• pip install tensorflow-datasets : Versi stabil, dirilis setiap beberapa bulan.
• pip install tfds-nightly : Dirilis setiap hari, berisi versi terakhir dari kumpulan data.

Colab ini menggunakan 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

Temukan kumpulan data yang tersedia

Semua pembuat kumpulan data adalah subkelas dari tfds.core.DatasetBuilder . Untuk mendapatkan daftar builder yang tersedia, gunakan tfds.list_builders() atau lihat katalog kami.

tfds.list_builders()

['abstract_reasoning',
 'accentdb',
 'aeslc',
 'aflw2k3d',
 'ag_news_subset',
 'ai2_arc',
 'ai2_arc_with_ir',
 'amazon_us_reviews',
 'anli',
 'arc',
 'asset',
 'assin2',
 'bair_robot_pushing_small',
 'bccd',
 'beans',
 'bee_dataset',
 'big_patent',
 'bigearthnet',
 'billsum',
 'binarized_mnist',
 'binary_alpha_digits',
 'blimp',
 'booksum',
 'bool_q',
 'c4',
 'caltech101',
 'caltech_birds2010',
 'caltech_birds2011',
 'cardiotox',
 'cars196',
 'cassava',
 'cats_vs_dogs',
 'celeb_a',
 'celeb_a_hq',
 'cfq',
 'cherry_blossoms',
 'chexpert',
 'cifar10',
 'cifar100',
 'cifar10_1',
 'cifar10_corrupted',
 'citrus_leaves',
 'cityscapes',
 'civil_comments',
 'clevr',
 'clic',
 'clinc_oos',
 'cmaterdb',
 'cnn_dailymail',
 'coco',
 'coco_captions',
 'coil100',
 'colorectal_histology',
 'colorectal_histology_large',
 'common_voice',
 'coqa',
 'cos_e',
 'cosmos_qa',
 'covid19',
 'covid19sum',
 'crema_d',
 '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',
 'davis',
 'deep_weeds',
 'definite_pronoun_resolution',
 'dementiabank',
 'diabetic_retinopathy_detection',
 'diamonds',
 'div2k',
 'dmlab',
 'doc_nli',
 'dolphin_number_word',
 'domainnet',
 'downsampled_imagenet',
 'drop',
 'dsprites',
 'dtd',
 'duke_ultrasound',
 'e2e_cleaned',
 'efron_morris75',
 'emnist',
 'eraser_multi_rc',
 'esnli',
 'eurosat',
 'fashion_mnist',
 'flic',
 'flores',
 'food101',
 'forest_fires',
 'fuss',
 'gap',
 'geirhos_conflict_stimuli',
 'gem',
 'genomics_ood',
 'german_credit_numeric',
 'gigaword',
 'glue',
 'goemotions',
 'gov_report',
 'gpt3',
 'gref',
 'groove',
 'grounded_scan',
 'gsm8k',
 'gtzan',
 'gtzan_music_speech',
 'hellaswag',
 'higgs',
 'horses_or_humans',
 'howell',
 'i_naturalist2017',
 'i_naturalist2018',
 'imagenet2012',
 'imagenet2012_corrupted',
 'imagenet2012_multilabel',
 'imagenet2012_real',
 'imagenet2012_subset',
 'imagenet_a',
 'imagenet_lt',
 'imagenet_r',
 'imagenet_resized',
 'imagenet_sketch',
 'imagenet_v2',
 'imagenette',
 'imagewang',
 'imdb_reviews',
 'irc_disentanglement',
 'iris',
 'istella',
 'kddcup99',
 'kitti',
 'kmnist',
 'lambada',
 'lfw',
 'librispeech',
 'librispeech_lm',
 'libritts',
 'ljspeech',
 'lm1b',
 'locomotion',
 'lost_and_found',
 'lsun',
 'lvis',
 'malaria',
 'math_dataset',
 'math_qa',
 'mctaco',
 'mlqa',
 'mnist',
 'mnist_corrupted',
 'movie_lens',
 'movie_rationales',
 'movielens',
 'moving_mnist',
 'mslr_web',
 'multi_news',
 'multi_nli',
 'multi_nli_mismatch',
 'natural_questions',
 'natural_questions_open',
 'newsroom',
 'nsynth',
 'nyu_depth_v2',
 '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',
 'plant_leaves',
 'plant_village',
 'plantae_k',
 'protein_net',
 'qa4mre',
 'qasc',
 'quac',
 'quality',
 'quickdraw_bitmap',
 'race',
 'radon',
 'reddit',
 'reddit_disentanglement',
 'reddit_tifu',
 'ref_coco',
 'resisc45',
 'rlu_atari',
 'rlu_atari_checkpoints',
 'rlu_atari_checkpoints_ordered',
 '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_rwrl',
 'robomimic_ph',
 'robonet',
 'robosuite_panda_pick_place_can',
 'rock_paper_scissors',
 'rock_you',
 's3o4d',
 'salient_span_wikipedia',
 'samsum',
 'savee',
 'scan',
 'scene_parse150',
 'schema_guided_dialogue',
 'scicite',
 'scientific_papers',
 'scrolls',
 'sentiment140',
 'shapes3d',
 'siscore',
 'smallnorb',
 'smartwatch_gestures',
 'snli',
 'so2sat',
 'speech_commands',
 'spoken_digit',
 'squad',
 'squad_question_generation',
 'stanford_dogs',
 'stanford_online_products',
 'star_cfq',
 'starcraft_video',
 'stl10',
 'story_cloze',
 'summscreen',
 'sun397',
 'super_glue',
 'svhn_cropped',
 'symmetric_solids',
 'tao',
 'ted_hrlr_translate',
 'ted_multi_translate',
 'tedlium',
 'tf_flowers',
 'the300w_lp',
 'tiny_shakespeare',
 'titanic',
 'trec',
 'trivia_qa',
 'tydi_qa',
 'uc_merced',
 'ucf101',
 'vctk',
 'visual_domain_decathlon',
 'voc',
 'voxceleb',
 'voxforge',
 'waymo_open_dataset',
 'web_nlg',
 'web_questions',
 'wider_face',
 'wiki40b',
 'wiki_auto',
 'wiki_bio',
 '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_xnli',
 'yelp_polarity_reviews',
 'yes_no',
 'youtube_vis',
 'huggingface:acronym_identification',
 'huggingface:ade_corpus_v2',
 '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:billsum',
 'huggingface:bing_coronavirus_query_set',
 'huggingface:biomrc',
 'huggingface:biosses',
 'huggingface:blbooksgenre',
 'huggingface:blended_skill_talk',
 'huggingface:blimp',
 'huggingface:blog_authorship_corpus',
 'huggingface:bn_hate_speech',
 '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:conll2000',
 'huggingface:conll2002',
 'huggingface:conll2003',
 'huggingface:conllpp',
 '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: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:eli5',
 'huggingface:eli5_category',
 'huggingface:emea',
 'huggingface:emo',
 'huggingface:emotion',
 'huggingface:emotone_ar',
 'huggingface:empathetic_dialogues',
 'huggingface:enriched_web_nlg',
 'huggingface:eraser_multi_rc',
 'huggingface:esnli',
 'huggingface:eth_py150_open',
 'huggingface:ethos',
 '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: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: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: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:menyo20k_mt',
 'huggingface:meta_woz',
 'huggingface:metooma',
 'huggingface:metrec',
 'huggingface:miam',
 'huggingface:mkb',
 'huggingface:mkqa',
 'huggingface:mlqa',
 'huggingface:mlsum',
 'huggingface:mnist',
 'huggingface:mocha',
 '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: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:quora',
 'huggingface:quoref',
 'huggingface:race',
 'huggingface:re_dial',
 'huggingface:reasoning_bg',
 'huggingface:recipe_nlg',
 'huggingface:reclor',
 '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:ronec',
 'huggingface:ropes',
 'huggingface:rotten_tomatoes',
 'huggingface:russian_super_glue',
 'huggingface:s2orc',
 'huggingface:samsum',
 'huggingface:sanskrit_classic',
 'huggingface:saudinewsnet',
 'huggingface:sberquad',
 'huggingface:scan',
 'huggingface:scb_mt_enth_2020',
 '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',
 'huggingface:spanish_billion_words',
 'huggingface:spc',
 'huggingface:species_800',
 'huggingface:speech_commands',
 'huggingface:spider',
 'huggingface:squad',
 'huggingface:squad_adversarial',
 'huggingface:squad_es',
 'huggingface:squad_it',
 'huggingface:squad_kor_v1',
 'huggingface:squad_kor_v2',
 'huggingface:squad_v1_pt',
 'huggingface:squad_v2',
 'huggingface:squadshifts',
 'huggingface:srwac',
 'huggingface:sst',
 'huggingface:stereoset',
 'huggingface:story_cloze',
 'huggingface:stsb_mt_sv',
 'huggingface:stsb_multi_mt',
 'huggingface:style_change_detection',
 'huggingface:subjqa',
 'huggingface:super_glue',
 'huggingface:superb',
 'huggingface:swag',
 'huggingface:swahili',
 'huggingface:swahili_news',
 'huggingface:swda',
 'huggingface:swedish_medical_ner',
 'huggingface:swedish_ner_corpus',
 'huggingface:swedish_reviews',
 'huggingface:swiss_judgment_prediction',
 'huggingface:tab_fact',
 'huggingface:tamilmixsentiment',
 'huggingface:tanzil',
 'huggingface:tapaco',
 'huggingface:tashkeela',
 'huggingface:taskmaster1',
 'huggingface:taskmaster2',
 'huggingface:taskmaster3',
 'huggingface:tatoeba',
 'huggingface:ted_hrlr',
 'huggingface:ted_iwlst2013',
 'huggingface:ted_multi',
 'huggingface:ted_talks_iwslt',
 'huggingface:telugu_books',
 'huggingface:telugu_news',
 'huggingface:tep_en_fa_para',
 'huggingface:thai_toxicity_tweet',
 'huggingface:thainer',
 'huggingface:thaiqa_squad',
 'huggingface:thaisum',
 'huggingface:the_pile',
 'huggingface:the_pile_books3',
 'huggingface:the_pile_openwebtext2',
 'huggingface:the_pile_stack_exchange',
 'huggingface:tilde_model',
 'huggingface:time_dial',
 'huggingface:times_of_india_news_headlines',
 'huggingface:timit_asr',
 'huggingface:tiny_shakespeare',
 'huggingface:tlc',
 'huggingface:tmu_gfm_dataset',
 'huggingface:totto',
 'huggingface:trec',
 'huggingface:trivia_qa',
 'huggingface:tsac',
 'huggingface:ttc4900',
 'huggingface:tunizi',
 'huggingface:tuple_ie',
 'huggingface:turk',
 'huggingface:turkish_movie_sentiment',
 'huggingface:turkish_ner',
 'huggingface:turkish_product_reviews',
 'huggingface:turkish_shrinked_ner',
 'huggingface:turku_ner_corpus',
 'huggingface:tweet_eval',
 'huggingface:tweet_qa',
 'huggingface:tweets_ar_en_parallel',
 'huggingface:tweets_hate_speech_detection',
 'huggingface:twi_text_c3',
 'huggingface:twi_wordsim353',
 'huggingface:tydiqa',
 'huggingface:ubuntu_dialogs_corpus',
 'huggingface:udhr',
 'huggingface:um005',
 'huggingface:un_ga',
 'huggingface:un_multi',
 'huggingface:un_pc',
 'huggingface:universal_dependencies',
 'huggingface:universal_morphologies',
 'huggingface:urdu_fake_news',
 'huggingface:urdu_sentiment_corpus',
 'huggingface:vctk',
 'huggingface:vivos',
 'huggingface:web_nlg',
 'huggingface:web_of_science',
 'huggingface:web_questions',
 'huggingface:weibo_ner',
 'huggingface:wi_locness',
 'huggingface:wiki40b',
 'huggingface:wiki_asp',
 'huggingface:wiki_atomic_edits',
 'huggingface:wiki_auto',
 'huggingface:wiki_bio',
 'huggingface:wiki_dpr',
 'huggingface:wiki_hop',
 'huggingface:wiki_lingua',
 'huggingface:wiki_movies',
 'huggingface:wiki_qa',
 'huggingface:wiki_qa_ar',
 'huggingface:wiki_snippets',
 'huggingface:wiki_source',
 'huggingface:wiki_split',
 'huggingface:wiki_summary',
 'huggingface:wikiann',
 'huggingface:wikicorpus',
 'huggingface:wikihow',
 'huggingface:wikipedia',
 'huggingface:wikisql',
 'huggingface:wikitext',
 'huggingface:wikitext_tl39',
 'huggingface:wili_2018',
 'huggingface:wino_bias',
 'huggingface:winograd_wsc',
 'huggingface:winogrande',
 'huggingface:wiqa',
 'huggingface:wisesight1000',
 'huggingface:wisesight_sentiment',
 ...]

Muat kumpulan data

tfds.load

Cara termudah untuk memuat kumpulan data adalah tfds.load . Itu akan:

1. Unduh data dan simpan sebagai file tfrecord .
2. Muat tfrecord dan buat tf.data.Dataset .

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

<_OptionsDataset element_spec={'image': TensorSpec(shape=(28, 28, 1), dtype=tf.uint8, name=None), 'label': TensorSpec(shape=(), dtype=tf.int64, name=None)}>
2022-02-07 04:07:40.542243: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected

Beberapa argumen umum:

• split= : Pemisahan mana yang harus dibaca (misalnya 'train' , ['train', 'test'] , 'train[80%:]' ,...). Lihat panduan API terpisah kami.
• shuffle_files= : Mengontrol apakah akan mengacak file di antara setiap zaman (TFDS menyimpan kumpulan data besar dalam beberapa file yang lebih kecil).
• data_dir= : Lokasi penyimpanan dataset ( defaultnya ~/tensorflow_datasets/ )
• with_info=True : Mengembalikan tfds.core.DatasetInfo yang berisi metadata kumpulan data
• download=False : Nonaktifkan download

tfds.builder

tfds.load adalah pembungkus tipis di sekitar tfds.core.DatasetBuilder . Anda bisa mendapatkan hasil yang sama menggunakan tfds.core.DatasetBuilder API:

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)

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

tfds build CLI

Jika Anda ingin membuat kumpulan data tertentu, Anda dapat menggunakan baris perintah tfds . Sebagai contoh:

tfds build mnist

Lihat dokumen untuk tanda yang tersedia.

Iterasi pada kumpulan data

Seperti dikte

Secara default, objek tf.data.Dataset berisi dict dari tf.Tensor s:

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)
2022-02-07 04:07:41.932638: W tensorflow/core/kernels/data/cache_dataset_ops.cc:768] 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.

Untuk mengetahui nama dan struktur kunci dict , lihat dokumentasi kumpulan data di katalog kami . Misalnya: dokumentasi mnist .

Sebagai Tuple ( as_supervised=True )

Dengan menggunakan as_supervised=True , Anda bisa mendapatkan Tuple (features, label) sebagai gantinya untuk kumpulan data yang diawasi.

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)
2022-02-07 04:07:42.593594: W tensorflow/core/kernels/data/cache_dataset_ops.cc:768] 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.

Sebagai numpy ( tfds.as_numpy )

Menggunakan tfds.as_numpy untuk mengonversi:

• tf.Tensor -> np.array
• tf.data.Dataset -> Iterator[Tree[np.array]] ( Tree dapat berupa Dict bersarang sewenang-wenang, 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
2022-02-07 04:07:43.220027: W tensorflow/core/kernels/data/cache_dataset_ops.cc:768] 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.

Sebagai kumpulan tf.Tensor ( batch_size=-1 )

Dengan menggunakan batch_size=-1 , Anda dapat memuat kumpulan data lengkap dalam satu kumpulan.

Ini dapat dikombinasikan dengan as_supervised=True dan tfds.as_numpy untuk mendapatkan data sebagai (np.array, np.array) :

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)

Berhati-hatilah agar kumpulan data Anda dapat masuk ke dalam memori, dan semua contoh memiliki bentuk yang sama.

Tolok ukur set data Anda

Membandingkan dataset adalah panggilan tfds.benchmark sederhana pada setiap iterable (misalnya 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) 42295.82 ex/sec (total: 60000 ex, 1.42 sec)
Examples/sec (First only) 131.50 ex/sec (total: 32 ex, 0.24 sec)
Examples/sec (First excluded) 51026.08 ex/sec (total: 59968 ex, 1.18 sec)

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

Examples/sec (First included) 204278.25 ex/sec (total: 60000 ex, 0.29 sec)
Examples/sec (First only) 1444.72 ex/sec (total: 32 ex, 0.02 sec)
Examples/sec (First excluded) 220821.83 ex/sec (total: 59968 ex, 0.27 sec)

• Jangan lupa untuk menormalkan hasil per batch size dengan batch_size= kwarg.
• Singkatnya, batch pemanasan pertama dipisahkan dari yang lain untuk menangkap waktu penyiapan tambahan tf.data.Dataset (misalnya inisialisasi buffer,...).
• Perhatikan bagaimana iterasi kedua jauh lebih cepat karena TFDS auto-caching .
• tfds.benchmark mengembalikan tfds.core.BenchmarkResult yang dapat diperiksa untuk analisis lebih lanjut.

Membangun pipa ujung-ke-ujung

Untuk melangkah lebih jauh, Anda dapat melihat:

• Contoh Keras end-to-end kami untuk melihat alur pelatihan penuh (dengan batching, shuffle,...).
• Panduan kinerja kami untuk meningkatkan kecepatan pipeline Anda (tip: gunakan tfds.benchmark(ds) untuk membandingkan set data Anda).

visualisasi

tfds.as_dataframe

objek tf.data.Dataset dapat dikonversi menjadi pandas.DataFrame dengan tfds.as_dataframe untuk divisualisasikan di Colab .

• Tambahkan tfds.core.DatasetInfo sebagai argumen kedua dari tfds.as_dataframe untuk memvisualisasikan gambar, audio, teks, video,...
• Gunakan ds.take(x) untuk hanya menampilkan x contoh pertama. pandas.DataFrame akan memuat set data lengkap dalam memori, dan bisa sangat mahal untuk ditampilkan.

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

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

2022-02-07 04:07:47.001241: W tensorflow/core/kernels/data/cache_dataset_ops.cc:768] 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 mengembalikan matplotlib.figure.Figure (hanya kumpulan data gambar yang didukung sekarang):

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

fig = tfds.show_examples(ds, info)

2022-02-07 04:07:48.083706: W tensorflow/core/kernels/data/cache_dataset_ops.cc:768] 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.

Akses metadata kumpulan data

Semua builder menyertakan objek tfds.core.DatasetInfo yang berisi metadata kumpulan data.

Dapat diakses melalui:

• API tfds.load :

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

• API tfds.core.DatasetBuilder :

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

Info kumpulan data berisi informasi tambahan tentang kumpulan data (versi, kutipan, beranda, deskripsi,...).

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_path='gs://tensorflow-datasets/datasets/mnist/3.0.1',
    download_size=11.06 MiB,
    dataset_size=21.00 MiB,
    features=FeaturesDict({
        'image': Image(shape=(28, 28, 1), dtype=tf.uint8),
        'label': ClassLabel(shape=(), dtype=tf.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}
    }""",
)

Fitur metadata (nama label, bentuk gambar,...)

Akses tfds.features.FeatureDict :

info.features

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

Jumlah kelas, nama label:

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

Bentuk, dtypes:

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

{'image': (28, 28, 1), 'label': ()}
{'image': tf.uint8, 'label': tf.int64}
(28, 28, 1)
<dtype: 'uint8'>

Pisahkan metadata (mis., pisahkan nama, jumlah contoh,...)

Akses tfds.core.SplitDict :

print(info.splits)

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

Perpecahan yang tersedia:

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

['test', 'train']

Dapatkan info tentang pemisahan individu:

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

60000
['gs://tensorflow-datasets/datasets/mnist/3.0.1/mnist-train.tfrecord-00000-of-00001']
1

Ini juga berfungsi dengan API subsplit:

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, num_examples=36000)]

Penyelesaian masalah

Unduhan manual (jika unduhan gagal)

Jika unduhan gagal karena alasan tertentu (mis. offline,...). Anda selalu dapat mengunduh sendiri data secara manual dan menempatkannya di manual_dir (defaultnya ~/tensorflow_datasets/download/manual/ .

Untuk mengetahui url mana yang akan diunduh, lihat:

• Untuk kumpulan data baru (diimplementasikan sebagai folder): tensorflow_datasets/ <type>/<dataset_name>/checksums.tsv . Misalnya: tensorflow_datasets/text/bool_q/checksums.tsv .

  Anda dapat menemukan lokasi sumber dataset di katalog kami .

• Untuk kumpulan data lama: tensorflow_datasets/url_checksums/<dataset_name>.txt

Memperbaiki NonMatchingChecksumError

TFDS memastikan determinisme dengan memvalidasi checksum dari url yang diunduh. Jika NonMatchingChecksumError dinaikkan, mungkin menunjukkan:

• Situs web mungkin sedang down (misalnya 503 status code ). Silakan periksa urlnya.
• Untuk URL Google Drive, coba lagi nanti karena Drive terkadang menolak unduhan saat terlalu banyak orang mengakses URL yang sama. Lihat bug
• File kumpulan data asli mungkin telah diperbarui. Dalam hal ini, pembuat kumpulan data TFDS harus diperbarui. Silakan buka masalah atau PR Github baru:
  • Daftarkan checksum baru dengan tfds build --register_checksums
  • Akhirnya perbarui kode pembuatan kumpulan data.
  • Perbarui kumpulan data VERSION
  • Perbarui dataset RELEASE_NOTES : Apa yang menyebabkan checksum berubah ? Apakah beberapa contoh berubah?
  • Pastikan dataset masih bisa dibangun.
  • Kirimkan PR kepada kami

Kutipan

Jika Anda menggunakan tensorflow-datasets untuk makalah, harap sertakan kutipan berikut, selain kutipan khusus untuk set data yang digunakan (yang dapat ditemukan di katalog dataset ).

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