Link
Embed
Share
Beginning
This slide
Copy link URL
Copy link URL
Copy iframe embed code
Copy iframe embed code
Copy javascript embed code
Copy javascript embed code
Share
Tweet
Share
Tweet
Slide 1
Slide 1 text
θϩ͔Β࡞ΔDeep Learning 2 ̏ষ word2vec 3.1ʙ3.2 ota42y θϩ͔Β࡞ΔDeep Learning 2 ࣗવݴޠฤ ಡॻձ ୈ5ճ
Slide 2
Slide 2 text
͜ͷষͰΔ͜ͱ • word2vecΛ࣮͢Δ • ਪϕʔεͰ୯ޠΛϕΫτϧͰද͢ํ๏ • γϯϓϧ͕ͩແବଟ͍࣮ • ࣍ͷষͰରԠ
Slide 3
Slide 3 text
3.1 ਪϕʔεͷख๏ͱ χϡʔϥϧωοτϫʔΫ
Slide 4
Slide 4 text
ਪϕʔεͷϕΫτϧԽ • ୯ޠΛϕΫτϧʹ͢Δ̎ͭͷख๏ • Χϯτϕʔεʢ̎ষʣ • ਪϕʔεʢ̏ষʣ • ͲͪΒԾઃΛϕʔεʹͯ͠Δ͕Ξϓϩʔνશ͘ผ • Ծઃɿ୯ޠͷҙຯपғͷ୯ޠ͔Βܗ͞ΕΔ (p.67)
Slide 5
Slide 5 text
3.1.1ɹΧϯτϕʔεͷख๏ͷ • Χϯτϕʔεपғͷ୯ޠͷසΛܭࢉ͢Δ • ޠኮ͕nͩͱn*nͷڊେͳڞىߦྻ͕ඞཁʹͳΔ • ࣍ݩݮͷͨΊͷSVDO(n^3)ͷܭࢉྔɺ͍
Slide 6
Slide 6 text
ਪϕʔεͷར • Χϯτϕʔείʔύεશମͷ౷ܭσʔλΛҰؾʹར༻͢Δ • ਪϕʔε(χϡʔϥϧωοτ)ίʔύεͷҰ෦Ͱֶश͢Δ • GPUͷฒྻܭࢉฉ͘ • খ͚ʹͰ͖ɺߴʹฒྻॲཧͰ͖ΔͷͰڊେσʔλͰରԠͰ͖Δ • ଞʹັྗతͳ͕͋Δ(Β͍͠ɺৄ͘͠3.5.3)
Slide 7
Slide 7 text
3.1.2ɹਪϕʔεͷख๏ͷ֓ཁ
Slide 8
Slide 8 text
पғͷ୯ޠ͔Β୯ޠΛʮਪʯ͢Δ • `?`ʹԿ͕ೖΔ͔Λલޙ͔Βਪ • ίϯςΩετ͔ΒλʔήοτΛਪ • ίϯςΩετɿपғͷ୯ޠ(you, goodby) • λʔήοτɿରͷ୯ޠ(`?`)
Slide 9
Slide 9 text
ਪ݁Ռ • ֤୯ޠ͕ͦ͜ʹݱΕΔ֬Λग़ྗ • ίϯςΩετΛϞσϧʹ༩͑Δͱ୯ޠͷ͕֬ಘΒΕΔ
Slide 10
Slide 10 text
3.1.3 χϡʔϥϧωοτϫʔΫʹ͓͚Δ୯ ޠͷॲཧํ๏ • χϡʔϥϧωοτϫʔΫ(NN)ͷೖྗݻఆϕΫτϧ • ୯ޠΛͦͷ··ೖΕΔͷ͍͠ • ୯ޠΛone-hotදݱ(one-hotϕΫτϧ)ʹม͢Δ
Slide 11
Slide 11 text
one-hotදݱ • ޠኮͷ͞Λ࣋ͪɺ୯ޠIDͱ֘͢Δ෦͕1ɺͦΕҎ֎͕0 ͷϕΫτϧ • ͯ͢ͷ୯ޠΛಉ͡͞ͷϕΫτϧͱͯ͠දݱ
Slide 12
Slide 12 text
one-hotදݱ • શ݁߹Ͱม͢ΔͳΒ؆୯(ྫதؒ=3)
Slide 13
Slide 13 text
αϯϓϧίʔυ(p.99) • np.dot(c, W)୯ޠʹରԠ͢ΔॏΈΛऔΓग़ͯ͠Δ͚ͩ • W[0]ͷσʔλΛऔΓग़ͯ͠Δ͚ͩ • ແବͬΆ͍͕࣍ͷষͰ࣏͢Β͍͠
Slide 14
Slide 14 text
ϨΠϠදݱ • MatMulϨΠϠ(p.30)Ͱಉ͜͡ͱ͕Ͱ͖Δ • np.dot͢Δ͚ͩͷϨΠϠͳͷͰ
Slide 15
Slide 15 text
3.2ɹγϯϓϧͳword2vec
Slide 16
Slide 16 text
word2vecΛ࣮͢Δ • word2vecͰΘΕΔϞσϧCROWϞσϧͱskip-gramϞσϧ • "word2vec"͕͜ΕΒͷϞσϧΛࢦ͢߹͋Δ • ຊདྷͷҙຯͱζϨͯΔ
Slide 17
Slide 17 text
3.2.1 CBOWϞσϧͷਪॲཧ • ίϯςΩετ͔ΒλʔήοτΛਪଌ͢ΔNN • ίϯςΩετʹपғͷ୯ޠ • λʔήοτʹରͷ୯ޠ
Slide 18
Slide 18 text
୯ޠͷࢄදݱ • CBOWϞσϧΛ܇࿅͢Δ͜ͱͰ୯ޠͷࢄදݱΛಘΒΕΔ • Ϟσϧͷύϥϝʔλ͕ࢄදݱʹରԠ͢Δ
Slide 19
Slide 19 text
CBOWϞσϧͷશମ૾ • ίϯςΩετʹ̎ɺӅΕʹ̏ͷ߹
Slide 20
Slide 20 text
CBOWϞσϧͷશମ૾ • ೖྗෳݸͷone-hotදݱͷ୯ޠ • ग़ྗ֤୯ޠͷείΞ • softmaxΛ͏ͱ͕֬ಘΒΕΔ • தؒೖྗ͔Βͷͷฏۉ
Slide 21
Slide 21 text
• ࢄදݱͷਖ਼ମ • [$ W_{in}]7*3ͷॏΈ • ͜Ε͕୯ޠͷࢄදݱ • ֶशʹΑͬͯྑ͍ࢄදݱʹ͍ͯ͘͠
Slide 22
Slide 22 text
CBOWϞσϧͷϨΠϠදݱ
Slide 23
Slide 23 text
CBOWϞσϧͷϨΠϠදݱ • ̎ͭͷMatMulϨΠϠ • ୯ޠʹରԠ͢ΔॏΈΛऔΓग़ͭ͢(P.99) • ̎ͭͷฏۉΛऔΔ(=ͯ͠0.5Λ͔͚Δ) • scoreͷશ݁߹ • ׆ੑԽؔແ͍ͷͰΘΓͱγϯϓϧ
Slide 24
Slide 24 text
3.2.2 CBOWϞσϧͷֶश • χϡʔϥϧωοτϫʔΫͷηΦϦʔ௨Γ • CBOWଞΫϥεྨΛ͢ΔNN • Ϋϥεʹone-hotͰද͞Εͨ୯ޠ • είΞ͔Β֬ΛٻΊͯɺਖ਼ղͱͷࠩΛֶश͢Δ • Softmaxؔʹ͔͚ͯ֬ʹ͢Δ • ڭࢣϥϕϧ͔ΒަࠩΤϯτϩϐʔޡࠩΛٻΊΔ
Slide 25
Slide 25 text
ϨΠϠදݱ • Softmax with lossΛ͚Ճ͑Δ
Slide 26
Slide 26 text
ίʔυϦʔσΟϯά • ch03/cbow_predict.py • https://github.com/oreilly-japan/deep-learning-from- scratch-2/blob/master/ch03/cbow_predict.py
Slide 27
Slide 27 text
3.2.3 word2vecͷॏΈͱࢄදݱ • ͱɹɹͷҧ͍ • ྆ํͱ୯ޠͷҙຯ͕Τϯίʔυ͞Ε͍ͯΔ • ܗঢ়͕ҧ͏ • ɹɹ7x3 • ɹɹ3x7 Win Wout Win Wout
Slide 28
Slide 28 text
ࢄදݱɹɹΛ͏ • ɹɹ શ͘Θͳ͍ɹ • ɹɹʹର͢Δskip-ngramͰͷ༗༻ੑ࣮ݧ • https://arxiv.org/abs/1611.01462 • ɹɹ͏͜ͱͰΑ͍݁Ռ͕ಘΒΕΔͱ͍͏ใࠂ • https://nlp.stanford.edu/projects/glove/ • word2vecͱࣅ͍ͯΔͭͷख๏ Win Win Wout Wout