Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
ML Ops Study 2
Search
ARIYAMA Keiji
May 29, 2018
Technology
0
130
ML Ops Study 2
5月29日にクックパッド株式会社で開催されたML Ops Study #2の発表資料です。
ARIYAMA Keiji
May 29, 2018
Tweet
Share
More Decks by ARIYAMA Keiji
See All by ARIYAMA Keiji
Build with AI
keiji
0
150
DroidKaigi 2023
keiji
0
1.6k
TechFeed Conference 2022
keiji
0
230
Android Bazaar and Conference Diverse 2021 Winter
keiji
0
850
ci-cd-conference-2021
keiji
1
1.2k
Android Bazaar and Conference 2021 Spring
keiji
3
760
TFUG KANSAI 20190928
keiji
0
94
Softpia Japan Seminar 20190724
keiji
1
150
pixiv App Night 20190611
keiji
1
560
Other Decks in Technology
See All in Technology
Turing × atmaCup #18 - 1st Place Solution
hakubishin3
0
490
Wantedly での Datadog 活用事例
bgpat
1
500
WACATE2024冬セッション資料(ユーザビリティ)
scarletplover
0
210
[Ruby] Develop a Morse Code Learning Gem & Beep from Strings
oguressive
1
170
Amazon Kendra GenAI Index 登場でどう変わる? 評価から学ぶ最適なRAG構成
naoki_0531
0
110
GitHub Copilot のテクニック集/GitHub Copilot Techniques
rayuron
37
14k
20241220_S3 tablesの使い方を検証してみた
handy
4
590
Fanstaの1年を大解剖! 一人SREはどこまでできるのか!?
syossan27
2
170
How to be an AWS Community Builder | 君もAWS Community Builderになろう!〜2024 冬 CB募集直前対策編?!〜
coosuke
PRO
2
2.8k
Snykで始めるセキュリティ担当者とSREと開発者が楽になる脆弱性対応 / Getting started with Snyk Vulnerability Response
yamaguchitk333
2
190
Oracle Cloud Infrastructure:2024年12月度サービス・アップデート
oracle4engineer
PRO
0
190
普通のエンジニアがLaravelコアチームメンバーになるまで
avosalmon
0
100
Featured
See All Featured
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
47
5.1k
Fight the Zombie Pattern Library - RWD Summit 2016
marcelosomers
232
17k
Agile that works and the tools we love
rasmusluckow
328
21k
"I'm Feeling Lucky" - Building Great Search Experiences for Today's Users (#IAC19)
danielanewman
226
22k
Design and Strategy: How to Deal with People Who Don’t "Get" Design
morganepeng
127
18k
We Have a Design System, Now What?
morganepeng
51
7.3k
[Rails World 2023 - Day 1 Closing Keynote] - The Magic of Rails
eileencodes
33
2k
The Myth of the Modular Monolith - Day 2 Keynote - Rails World 2024
eileencodes
17
2.3k
Practical Orchestrator
shlominoach
186
10k
Site-Speed That Sticks
csswizardry
2
190
Writing Fast Ruby
sferik
628
61k
Mobile First: as difficult as doing things right
swwweet
222
9k
Transcript
C-LIS CO., LTD.
.-0QT4UVEZ BUΫοΫύουגࣜձࣾ 5FOTPS'MPXͷ܇࿅ࡁΈϞσϧΛ "OESPJEΞϓϦʹࡌͤΔͱ͖ʹ ۤ࿑ͨ͠ 5FOTPS'MPXͰझຯͷը૾ऩूαʔόʔΛ࡞Δ݄߸
C-LIS CO., LTD. ༗ࢁܓೋʢ,FJKJ"3*:"."ʣ $-*4$0 -5% Photo :
Koji MORIGUCHI (MORIGCHOWDER) "OESPJEΞϓϦ։ൃνϣοτσΩϧ ػցֶशͪΐͬͱͬͨ͜ͱ͋Γ·͢
C-LIS CO., LTD.
C-LIS CO., LTD. ͬ
ຊ൪ڥͷೖܦݧ͕ඞཁͳΒ ೖͯ͠͠·͍͍͑͡Όͳ͍
؟ ڸ ͬ ່ ࠜઇΕ͍
Έͷ؟ڸ່ͬը૾Λ ࣗಈͰऩू͍ͨ͠
̎Ϋϥεྨ 1 0
ݱࡏͷγεςϜ
ධՁ༻ αʔόʔ ܇࿅ɾֶश༻αʔόʔ σʔληοτసૹ ʢTFRecordʣ ֶशࡁΈ ύϥϝʔλʔऔಘ ը૾औಘ
ը૾औಘ ϥϕϧ ͚ σʔληοτཧ αʔόʔ σʔλऩूݩ αʔϏε ը૾औಘ ϥϕϧ ͚ Android ΞϓϦ
σʔληοτཧαʔόʔ $16"UISPO/FP()[ .FNPSZ(# 4UPSBHF44%(# )%%5# 3"*%
σʔληοτཧαʔόʔͷׂ ը૾σʔλͷऩू ϝλσʔλʢΞϊςʔγϣϯɾϥϕϧʣͷཧ "1*ͷఏڙ ֶश༻σʔλʢ5'3FDPSEʣͷੜ
ը૾σʔλͷऩू
ϝλσʔλͷཧ
ϝλσʔλͷཧ label: 2 left: 283 top: 190 right:
435 bottom:301 = 1.0
ϥϕϧͷछྨ PSJHJOBM@BSU OTGX GBWPSJUF QIPUP JMMVTU DPNJD GBDF
GFNBMF NFHBOF TDISPPM@VOJGPSN CMB[FS@VOJGPSN TBJMPS@VOJGPSN HM LFNPOP NBMF CM DBU EPH GPPE EJTMJLF
"1*ͷఏڙ ը૾Ϧετͷऔಘ ը૾ͷऔಘ ը૾ͷݕࡧʢϥϕϧʣ ϥϕϧͷઃఆ ϥϕϧະઃఆը૾ͷϦετΛऔಘ σʔλऔಘݩ5XJUUFS*%ͷՃɾআ
ֶश༻σʔλͷੜ 5'3FDPSEܗࣜPS+1&( $47ܗࣜ ը૾ͷϦαΠζ͜ͷஈ֊Ͱߦ͏ʢτϥϑΟοΫΛݮ $ python ./create_dataset.py \
--base_dir /dataset/source/ \ --output_dir ~/tfrecords_classifier \ --image_size 256 \ --tag_names megane,nsfw,favorite,illust
σʔλͷάϧʔϓԽ 0 1 2 3 4 5 6
7 8 9 σʔληοτ ςετσʔληοτ
ධՁ༻ αʔόʔ ܇࿅ɾֶश༻αʔόʔ σʔληοτసૹ ʢTFRecordʣ ֶशࡁΈ ύϥϝʔλʔऔಘ ը૾औಘ
ը૾औಘ ϥϕϧ ͚ σʔληοτཧ αʔόʔ σʔλऩूݩ αʔϏε ը૾औಘ ϥϕϧ ͚ Android ΞϓϦ
ֶश༻αʔόʔ
{ "tag_name": "megane", "train_catalog_numbers": "0,1,2,3,4,5,6,7,8", "eval_catalog_numbers": "9", "data_augmentation": { "random_crop":
false, "random_colorize": true } } ֶश༻ͷઃఆϑΝΠϧ
ֶशͱݕূͷ࣮ߦ $ CUDA_VISIBLE_DEVICES=0,1 python ./train.py \ --learning_config config_megane.json
\ --tfrecords_dir ~/tfrecords_classifier \ --train_dir ~/train_single_discriminator \ --summary_dir ~/summary_single_discriminator \ --batch_size 64 \ --learning_rate 0.0001 \ --num_gpus 2 \ --max_step 100000 $ CUDA_VISIBLE_DEVICES=2 python ./eval.py \ --learning_config config_megane.json \ --tfrecords_dir ~/tfrecords_classifier \ --train_dir ~/train_single_discriminator \ --summary_dir ~/summary_single_discriminator $ tensorboard \ --logdir ~/summary_single_discriminator/megane/
ධՁ༻ αʔόʔ ܇࿅ɾֶश༻αʔόʔ σʔληοτసૹ ʢTFRecordʣ ֶशࡁΈ ύϥϝʔλʔऔಘ ը૾औಘ
ը૾औಘ ϥϕϧ ͚ σʔληοτཧ αʔόʔ σʔλऩूݩ αʔϏε ը૾औಘ ϥϕϧ ͚ Android ΞϓϦ
ධՁ༻αʔόʔ
ධՁͷ࣮ߦ $ python3 client.py \ --tag_name megane \
--train_base_path ~/train_single_discriminator \ --train_file_name precision-0.956463/megane.ckpt-294000 \ --batch_size 16 \ --limit_batch 100
ධՁ༻ αʔόʔ ܇࿅ɾֶश༻αʔόʔ σʔληοτసૹ ʢTFRecordʣ ֶशࡁΈ ύϥϝʔλʔऔಘ ը૾औಘ
ը૾औಘ ϥϕϧ ͚ σʔληοτཧ αʔόʔ σʔλऩूݩ αʔϏε ը૾औಘ ϥϕϧ ͚ Android ΞϓϦ
"OESPJEΞϓϦ
ۙͷ՝
ෆదը૾͕ଟ͗͢Δ
ෆదʢ/4'8ʣը૾ͱ؟ڸը૾ /4'8 positive: 36,083 → 7.17% negative:
466,738 ؟ڸ positive: 23,559 → 2.44% negative: 938,563
ෆదը૾ϑΟϧλʔΛ ΞϓϦʹΈࠐΉ
NPEFM NSFW positive: 5,628 negative: 17,253
NPEFM ֶशࡁΈύϥϝʔλʔϑΝΠϧ ֶशࡁΈϞσϧ .pb 170MB
NPEFM@MJUF ֶशࡁΈύϥϝʔλʔϑΝΠϧ ֶशࡁΈϞσϧ .pb 10.7MB
NPEFM@MJUF
Ϟσϧͷߏ .pb input result 128x128x3 1
private val IMAGE_WIDTH = 128 private val IMAGE_HEIGHT = 128
private val IMAGE_CHANNEL = 3 private val IMAGE_BYTES_LENGTH = IMAGE_WIDTH * IMAGE_HEIGHT * IMAGE_CHANNEL val imageByteBuffer: ByteBuffer = ByteBuffer.allocate(IMAGE_BYTES_LENGTH) val scaledBitmap = Bitmap.createScaledBitmap(bitmap, IMAGE_WIDTH, IMAGE_HEIGHT, false) scaledBitmap.copyPixelsToBuffer(imageByteBuffer) ը૾ΛόοϑΝʹ֨ೲ
val resultArray = FloatArray(1) fun recognize(imageByteArray: ByteArray): Float { val
start = Debug.threadCpuTimeNanos() tfInference.feed("input", imageByteArray, imageByteArray.size.toLong()) tfInference.run(arrayOf("result")) tfInference.fetch("result", resultArray) val elapsed = Debug.threadCpuTimeNanos() - start Log.d(TAG, "Elapsed: %d ns".format(elapsed)) return resultArray[0] } GFFESVOGFUDI
private val IMAGE_WIDTH = 128 private val IMAGE_HEIGHT = 128
private val IMAGE_CHANNEL = 3 private val IMAGE_BYTES_LENGTH = IMAGE_WIDTH * IMAGE_HEIGHT * IMAGE_CHANNEL val imageByteBuffer: ByteBuffer = ByteBuffer.allocate(IMAGE_BYTES_LENGTH) val scaledBitmap = Bitmap.createScaledBitmap(bitmap, IMAGE_WIDTH, IMAGE_HEIGHT, false) scaledBitmap.copyPixelsToBuffer(imageByteBuffer) scaledBitmap.recycle() ݪҼΒ͖͠ͷ ˢ"MQIBνϟϯωϧ͕ೖ͍ͬͯΔ
Ϟσϧͷߏ .pb input result 128x128x4 1
with tf.Graph().as_default() as g: image_ph = tf.placeholder( tf.uint8, [model.IMAGE_SIZE *
model.IMAGE_SIZE * 4], name='input') image = tf.cast(image_ph, tf.float32) image = tf.reshape( image, [model.IMAGE_SIZE, model.IMAGE_SIZE, 4]) image = image[:, :, :3] QCग़ྗ࣌ʹDIΛड͚ೖΕΔΑ͏ʹάϥϑΛมߋ
σϞ
'PPE(BMMFSZ https://github.com/keiji/food_gallery_with_tensorflow ΪϟϥϦʔʹอଘ͞Ε͍ͯΔ৯ͷը૾Λදࣔ http://techlife.cookpad.com/entry/2017/09/14/161756 ΫοΫύου։ൃऀϒϩά ྉཧ͖Ζ͘ʹ͓͚ΔྉཧʗඇྉཧผϞσϧͷৄࡉ
ΈࠐΜͰΈ͚ͨΕͲɺ ਫ਼͋·Γߴ͘ͳ͍ʜʜ
ࠓޙͷ՝ 5FOTPS'MPX-JUFͷҠߦ ߴਫ਼ͷϞσϧͷѹॖʢল༰ྔԽʣ 1SVOJOH 2VBOUJ[BUJPO %JTUJMMBUJPO ML Kit:
Machine Learning SDK for mobile developers (Google I/O '18) https://youtu.be/Z-dqGRSsaBs?t=32m10s
ࠓޙͷ՝ ֶशαΠΫϧΛࣗಈԽ͍͖͍ͯͨ͠ɻ ʢఆظతʹֶशσʔλͷੜͱసૹΛߦ͍ɺ࠶ֶश͢ΔͳͲʣ σʔλϕʔεͷߴԽ ɹϥϕϧݕࡧ͕ඇৗʹ͍ͷ͕՝ɻઃܭΛݟ͢ඞཁ͋Γ ɹݕࡧΠϯσοΫεͷ(PPHMF$MPVE4UPSFҠߦΛݕ౼
ࠓޙͷ՝ σʔληοτཧαʔόʔͷߋ৽ 5FOTPS'MPXΑΓɺ$16ʹ*OUFM"79͕ඞਢʹͳͬͨʢQJQ൛ʣ
C-LIS CO., LTD. ຊࢿྉɺ༗ݶձࣾγʔϦεͷஶ࡞Ͱ͢ɻຊࢿྉͷશ෦ɺ·ͨҰ෦ʹ͍ͭͯɺஶ࡞ऀ͔ΒจॻʹΑΔڐΛಘͣʹෳ͢Δ͜ͱې͡ΒΕ͍ͯ·͢ɻ 5IF"OESPJE4UVEJPJDPOJTSFQSPEVDFEPSNPEJpFEGSPNXPSLDSFBUFEBOETIBSFECZ(PPHMFBOEVTFEBDDPSEJOHUPUFSNTEFTDSJCFEJOUIF$SFBUJWF$PNNPOT"UUSJCVUJPO-JDFOTF ໊֤ɾϒϥϯυ໊ɺձ໊ࣾͳͲɺҰൠʹ֤ࣾͷඪ·ͨొඪͰ͢ɻຊࢿྉதͰɺɺɺäΛׂѪ͍ͯ͠·͢ɻ 5IF"OESPJESPCPUJTSFQSPEVDFEPSNPEJpFEGSPNXPSLDSFBUFEBOETIBSFECZ(PPHMFBOEVTFEBDDPSEJOHUPUFSNTEFTDSJCFEJOUIF$SFBUJWF$PNNPOT"UUSJCVUJPO-JDFOTF