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
20171209 Sakura ML Night
Search
ARIYAMA Keiji
December 09, 2017
Technology
0
150
20171209 Sakura ML Night
2017年12月9日に大阪で開催された「さくらの機械学習ナイト」の発表資料です。
「TensorFlowによるNSFW(職場で不適切な)画像検出」について。
ARIYAMA Keiji
December 09, 2017
Tweet
Share
More Decks by ARIYAMA Keiji
See All by ARIYAMA Keiji
Build with AI
keiji
0
210
DroidKaigi 2023
keiji
0
1.8k
TechFeed Conference 2022
keiji
0
280
Android Bazaar and Conference Diverse 2021 Winter
keiji
0
870
ci-cd-conference-2021
keiji
1
1.2k
Android Bazaar and Conference 2021 Spring
keiji
3
810
TFUG KANSAI 20190928
keiji
0
120
Softpia Japan Seminar 20190724
keiji
1
170
pixiv App Night 20190611
keiji
1
590
Other Decks in Technology
See All in Technology
Findy Freelance 利用シーン別AI活用例
ness
0
330
みんなのSRE 〜チーム全員でのSRE活動にするための4つの取り組み〜
kakehashi
PRO
2
140
マルチモーダル基盤モデルに基づく動画と音の解析技術
lycorptech_jp
PRO
4
550
人に寄り添うAIエージェントとアーキテクチャ #BetAIDay
layerx
PRO
8
2k
「AIと一緒にやる」が当たり前になるまでの奮闘記
kakehashi
PRO
3
100
Unson OS|48時間で「売れるか」を判定する AI 市場検証プラットフォーム
unson
0
170
アカデミーキャンプ 2025 SuuuuuuMMeR「燃えろ!!ロボコン」 / Academy Camp 2025 SuuuuuuMMeR "Burn the Spirit, Robocon!!" DAY 1
ks91
PRO
0
120
AIのグローバルトレンド 2025 / ai global trend 2025
kyonmm
PRO
1
120
相互運用可能な学修歴クレデンシャルに向けた標準技術と国際動向
fujie
0
210
AI時代の経営、Bet AI Vision #BetAIDay
layerx
PRO
1
1.8k
風が吹けばWHOISが使えなくなる~なぜWHOIS・RDAPはサーバー証明書のメール認証に使えなくなったのか~
orangemorishita
15
5.5k
Jamf Connect ZTNAとMDMで実現! 金融ベンチャーにおける「デバイストラスト」実例と軌跡 / Kyash Device Trust
rela1470
0
150
Featured
See All Featured
Adopting Sorbet at Scale
ufuk
77
9.5k
Stop Working from a Prison Cell
hatefulcrawdad
271
21k
ReactJS: Keep Simple. Everything can be a component!
pedronauck
667
120k
Music & Morning Musume
bryan
46
6.7k
Practical Tips for Bootstrapping Information Extraction Pipelines
honnibal
PRO
21
1.4k
Building an army of robots
kneath
306
45k
Fireside Chat
paigeccino
38
3.6k
How To Stay Up To Date on Web Technology
chriscoyier
790
250k
Building Adaptive Systems
keathley
43
2.7k
10 Git Anti Patterns You Should be Aware of
lemiorhan
PRO
656
60k
Designing Experiences People Love
moore
142
24k
Put a Button on it: Removing Barriers to Going Fast.
kastner
60
3.9k
Transcript
C-LIS CO., LTD.
C-LIS CO., LTD. ༗ࢁܓೋʢ,FJKJ"3*:"."ʣ $-*4$0 -5% "OESPJEΞϓϦ։ൃνϣοτσΩϧ Photo by
Koji MORIGUCHI (MORIGCHOWDER) ػցֶशͪΐͬͱͬͨ͜ͱ͋Γ·͢ Twitterͬͯ·ͤΜ
͘͞ΒͷػցֶशφΠτ 5FOTPS'MPXͰ /4'8ը૾ݕग़
5FOTPS'MPXʢ݄ൃදʣ ػցೳ͚ܭࢉϑϨʔϜϫʔΫ ࠷৽όʔδϣϯʢ݄ʣ
ษڧձΖ͏ͥ
(PPHMF%FWFMPQFS(SPVQ
IUUQTHEHLPCFEPPSLFFQFSKQFWFOUT
Πϯλʔωοτ͔Β Έͷը૾ΛࣗಈͰऩू͍ͨ͠
© ࠜઇΕ͍ ؟ ڸ ͬ ່
؟ڸ່ͬఆ 1 0
σʔληοτʢ݄࣌ʣ ؟ڸ່ͬɹຕ ඇ؟ڸ່ͬຕ ؟ڸ່ͬ ඇ؟ڸ່ͬ ޡݕग़ ؟ڸ່ͬ ඇ؟ڸ່ͬ
{ "generator": "Region Cropper", "file_name": "haruki_g17.png", "regions": [ { "probability":
1.0, "label": 2, "rect": { "left": 97.0, "top": 251.0, "right": 285.0, "bottom": 383.0 } }, { "probability": 1.0, "label": 2, "rect": { "left": 536.0, "top": 175.0, "right": 730.0, "bottom": 321.0 } } ] } Region Cropper: https://github.com/keiji/region_cropper
ߏ Downloader σʔληοτ Region + Label ઃఆ rsync
ཧͷߏ Downloader Face Detection Megane Detection ֬ೝɾमਖ਼ ೝࣝ݁Ռ ֶशʢ܇࿅ʣ
λΠϜϥΠϯ ϝσΟΞ σʔληοτ ֶशʢ܇࿅ʣ TensorFlow rsync
ઓͷաఔΛಉਓࢽʹ
͞·͟·ͳ՝ σʔληοτ͕(#Λ͑ͨ͋ͨΓ͔ΒϩʔΧϧͷಉظ͕ࠔʹɻ ྖҬʢ3FHJPOʣͷઃఆͱϥϕϧͷ༩૾Ҏ্ʹෛՙ͕ߴ͍ɻ
ը૾͕ສຕΛಥഁ σʔλཧ͕ࢸٸͷ՝ʹ
ඪΛ࠶֬ೝ
Πϯλʔωοτ͔Β Έͷ؟ڸ່ͬը૾ΛࣗಈͰऩू͍ͨ͠
Ҏલͷߏ Downloader σʔληοτ Region + Label ઃఆ rsync
ྖҬʴϥϕϧ
৽͍͠ߏ Downloader σʔληοτ Tagઃఆ
λά megane girl
؟ڸ່ͬผϞσϧ Ϟσϧ 1.00 0.00
%BUBTFU.BOBHFSGPS"OESPJE
σϞ
https://twitter.com/35s_00/status/930366666973757441
https://twitter.com/_meganeco
/4'8ʢ/PU4BGF'PS8PSLʣ
/4'8ը૾
͞·͟·ͳϦεΫ ࡞ۀͷϊΠζ ਫ਼ਆతͳෛՙ ๏తϦεΫ
/4'8ը૾ͷݕग़
ֶश༻σʔληοτʢ/4'8ʣ ਖ਼ྫɿ ෛྫɿ ← NSFWը૾
܇࿅ɾֶश
ڭࢣ༗Γֶश ◦ × Ϟσϧ 1.00 0.00
Ϟσϧͷߏ conv 3x3x64 stride 1 conv 3x3x64 stride 1
ReLU ReLU conv 3x3x128 stride 1 conv 3x3x128 stride 1 ReLU conv 3x3x256 stride 1 conv 3x3x256 stride 1 ReLU output 1 256x256x1 max_pool 2x2 stride 2 max_pool 2x2 stride 2 ReLU ReLU Sigmoid max_pool 2x2 stride 2 conv 3x3x64 stride 1 ReLU fc 768 ReLU bn bn bn
Sigmoid
# モデル定義 NUM_CLASSES = 1 NAME = 'model3' IMAGE_SIZE =
256 CHANNELS = 3 def prepare_layers(image, training=False): with tf.variable_scope('inference'): conv1 = tf.layers.conv2d(image, 64, [3, 3], [1, 1], padding='SAME', activation=tf.nn.relu, use_bias=False, trainable=training, name='conv1_1') conv1 = tf.layers.conv2d(conv1, 64, [3, 3], [1, 1], padding='VALID', activation=tf.nn.relu, use_bias=False, trainable=training, name='conv1_2') conv1 = tf.layers.batch_normalization(conv1, trainable=training, name='bn_1')
conv2 = tf.layers.conv2d(pool1, 128, [3, 3], [1, 1], padding='VALID', activation=tf.nn.relu,
use_bias=False, trainable=training, name='conv2_1') conv2 = tf.layers.conv2d(conv2, 128, [3, 3], [1, 1], padding='VALID', activation=tf.nn.relu, use_bias=False, trainable=training, name='conv2_2') conv2 = tf.layers.batch_normalization(conv2, trainable=training, name='bn_2') pool2 = tf.layers.max_pooling2d(conv2, [2, 2], [2, 2])
conv3 = tf.layers.conv2d(pool2, 256, [3, 3], [1, 1], padding='VALID', activation=tf.nn.relu,
use_bias=False, trainable=training, name='conv4_1') conv3 = tf.layers.conv2d(conv3, 256, [3, 3], [1, 1], padding='VALID', activation=tf.nn.relu, use_bias=False, trainable=training, name='conv4_2') conv3 = tf.layers.batch_normalization(conv3, trainable=training, name='bn_4') pool3 = tf.layers.max_pooling2d(conv3, [2, 2], [2, 2]) conv = tf.layers.conv2d(pool3, 64, [1, 1], [1, 1], padding='VALID', activation=tf.nn.relu, use_bias=True, trainable=training, name='conv') return conv
def output_layers(prev, batch_size, keep_prob=0.8, training=False): flatten = tf.reshape(prev, [batch_size, -1])
fc1 = tf.layers.dense(flatten, 768, trainable=training, activation=tf.nn.relu, name='fc1') fc1 = tf.layers.dropout(fc1, rate=keep_prob, training=training) output = tf.layers.dense(fc1, NUM_CLASSES, trainable=training, activation=None, name='output') return output
def _loss(logits, labels, batch_size, positive_ratio): cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits( labels=labels, logits=logits)
loss = tf.reduce_mean(cross_entropy) return loss def _init_optimizer(learning_rate): return tf.train.AdamOptimizer(learning_rate=learning_rate) ޡࠩؔͱ࠷దԽΞϧΰϦζϜ
ֶशΛ্ख͘ਐΊΔ
ਖ਼ྫɾෛྫͷൺ ਖ਼ྫɿ ෛྫɿ ← NSFWը૾ NSFW
def _hard_negative_mining(loss, labels, batch_size): positive_count = tf.reduce_sum(labels) positive_count = tf.reduce_max((positive_count,
1)) negative_count = positive_count * HARD_SAMPLE_MINING_RATIO negative_count = tf.reduce_max((negative_count, 1)) negative_count = tf.reduce_min((negative_count, batch_size)) positive_losses = loss * labels negative_losses = loss - positive_losses top_negative_losses, _ = tf.nn.top_k(negative_losses, k=tf.cast(negative_count, tf.int32)) loss = (tf.reduce_sum(positive_losses / positive_count) + tf.reduce_sum(top_negative_losses / negative_count)) return loss )BSE/FHBUJWF.JOJOH
ֶशڥʢ͘͞ΒͷߴՐྗίϯϐϡʔςΟϯάʣ $169FPO$PSFʷ .FNPSZ(# 44%(# (F'PSDF(595*5"/9ʢ1BTDBMΞʔΩςΫνϟʣ(#ʷ (F'PSDF(595Jʢ1BTDBMΞʔΩςΫνϟʣ(#ʷ
ֶश݅ ޡࠩؔަࠩΤϯτϩϐʔ ࠷దԽΞϧΰϦζϜ"EBN ֶश όοναΠζ
طଘͷσʔληοτʹਪʢJOGFSFODFʣΛ࣮ߦ Downloader σʔληοτ Tagઃఆ inference trainer ֶशࡁΈϞσϧ ֶश༻σʔληοτ
ਪ݁Ռ /4'8 Ұൠը૾ NSFW 8.6%
ֶश༻σʔληοτʢ/4'8ʣ ਖ਼ྫɿ ɹˠɹ ෛྫɿ ɹˠɹ
܇࿅ɾֶशʹ͔͔Δܭࢉ࣌ؒ
σϞ (16ɾ$16ͷൺֱ
$16ɾ(16ͷൺֱʢCBUDI4J[Fʣ 5*5"/9 TFDTUFQ 9FPO$PSF TFDTUFQ ࠓճͷϞσϧͷֶशʹ͍ͭͯ 5*5"/9ͷํ͕ഒ͍ʂ
$16ɾ(16ͷൺֱʢCBUDI4J[F ʣ 5*5"/9 (595J TFDTUFQ 9FPO$PSF TFDTUFQ
ࠓճͷϞσϧͷֶशʹ͍ͭͯ (16ʷͷํ͕ഒ͍ʂ
ࠓޙͷ՝
σʔληοταʔόʔͷ৴པੑ্
JOGFSFODFʢਪʣͷͨΊͷܭࢉࢿݯͷ֬อ Downloader σʔληοτ Tagઃఆ inference trainer ֶशࡁΈϞσϧ ֶश༻σʔληοτ
TAGS = [ 'original_art', 'nsfw', 'like', 'photo', 'illust', 'comic', 'face',
'girl', 'megane', ϥϕϧʢλάʣ 'school_uniform', 'blazer_uniform', 'sailor_uniform', 'gl', 'kemono', 'boy', 'bl', 'cat', 'dog', 'food', 'dislike', ]
.PWJEJVT
ਪΛ.PWJEJVTҠߦ Downloader σʔληοτ Tagઃఆ trainer ֶशࡁΈϞσϧ ֶश༻σʔληοτ inference
ΫϥεఆϞσϧ conv 3x3x64 stride 1 conv 3x3x64 stride 1
ReLU ReLU conv 3x3x128 stride 1 conv 3x3x128 stride 1 ReLU conv 3x3x256 stride 1 conv 3x3x256 stride 1 ReLU output 20 256x256x1 max_pool 2x2 stride 2 max_pool 2x2 stride 2 ReLU ReLU Sigmoid max_pool 2x2 stride 2 conv 3x3x64 stride 1 ReLU fc 768 ReLU bn bn bn
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 https://speakerdeck.com/keiji/20171209-sakura-ml-night