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
AI勉強会_Kerasハンズオン#1_分類
Search
taashi
April 17, 2019
Technology
0
95
AI勉強会_Kerasハンズオン#1_分類
社内勉強会資料
Keras
Google Colaboratory
(修正中...)
- precision, recallの説明追加
taashi
April 17, 2019
Tweet
Share
More Decks by taashi
See All by taashi
What is Haskell?
taashi
0
31
数学の世界~フラクタル~(社内勉強会1002)
taashi
0
100
論文LT会_論文紹介(CU-Net)
taashi
1
550
始めようElmでフロント開発_その02_ローカルWebアプリ
taashi
0
110
始めようElmでフロント開発_その01_Elmの基礎
taashi
0
130
Other Decks in Technology
See All in Technology
急成長する企業で作った、エンジニアが輝ける制度/ 20250214 Rinto Ikenoue
shift_evolve
2
880
Developer Summit 2025 [14-D-1] Yuki Hattori
yuhattor
19
5.1k
プロセス改善による品質向上事例
tomasagi
1
1.6k
株式会社EventHub・エンジニア採用資料
eventhub
0
4.2k
技術的負債解消の取り組みと専門チームのお話 #技術的負債_Findy
bengo4com
1
1.2k
これからSREになる人と、これからもSREをやっていく人へ
masayoshi
6
4.1k
SA Night #2 FinatextのSA思想/SA Night #2 Finatext session
satoshiimai
1
100
関東Kaggler会LT: 人狼コンペとLLM量子化について
nejumi
3
460
AndroidデバイスにFTPサーバを建立する
e10dokup
0
240
明日からできる!技術的負債の返済を加速するための実践ガイド~『ホットペッパービューティー』の事例をもとに~
recruitengineers
PRO
3
100
Larkご案内資料
customercloud
PRO
0
600
データ資産をシームレスに伝達するためのイベント駆動型アーキテクチャ
kakehashi
PRO
2
230
Featured
See All Featured
A Modern Web Designer's Workflow
chriscoyier
693
190k
The Success of Rails: Ensuring Growth for the Next 100 Years
eileencodes
44
7k
CSS Pre-Processors: Stylus, Less & Sass
bermonpainter
356
29k
Performance Is Good for Brains [We Love Speed 2024]
tammyeverts
7
630
Put a Button on it: Removing Barriers to Going Fast.
kastner
60
3.7k
Building Your Own Lightsaber
phodgson
104
6.2k
10 Git Anti Patterns You Should be Aware of
lemiorhan
PRO
656
59k
Rails Girls Zürich Keynote
gr2m
94
13k
Distributed Sagas: A Protocol for Coordinating Microservices
caitiem20
330
21k
[Rails World 2023 - Day 1 Closing Keynote] - The Magic of Rails
eileencodes
33
2.1k
The Power of CSS Pseudo Elements
geoffreycrofte
75
5.5k
ピンチをチャンスに:未来をつくるプロダクトロードマップ #pmconf2020
aki_iinuma
114
50k
Transcript
AI #1 (classification)
Keras
$# Google% Jupyter notebook$# Google Colaboratory (https://colab.research.google.com/)
12! " GPU, TPU! Google Drive &! … etc
Google Colab PYTHON Notebook
Colab!
(%"# ) : ! :
$ ! Notebook
$" !% GPU% #
GPU
%# & ’shift + enter’ "$ !
$’!’ " Linux !# Linux
ColabDrive&+ Google Drive! drive" Colb)' drive"%* from
google.colab import drive drive.mount('/content/drive') "( google.colabdrive ! Drive.mount$# Google Drive!
' !# URL (& ) Google
Drive% "$ (1)
$ # %! & ' " (’enter’ Google
Drive (2)
E@$#%"! <; *(7- ?.=384 :E@ 96 <;7 <;&'!Slack>
1, PLAYGROUND / 0C D5)+A - $# -2B
,89 Team Drives*(@2)/$#!7 "' 10Team Drives$#!5< (3:-
…) # [001] Install use items &% !.+ Team Drives $#!"' .+46 =; >? # [002] Mount GoogleDrive # [003] Copy data from google drive # [004] Check directory structure
Keras")* $&! '&
% $ )*#( )* $
:'<$ /1 !% '<9 '<7 *)0
(C F ) '<!% '<93" !5 '<9> (,?) '< 8) CF'<9A'< D+('24)(6. 8) FBC;&C'<-= (@+ #E)
Keras PythonA 8<BNNW0!,01 +($"4*TensorFlow, CNTK, Theano ; ID-3)'!- @
CPUGPU6%.2&;@ … etc :>A =?7#&&/ (=?7 ) 59 (JH) E : =?7 GCF
#$ # [005] Define constant value # [006] Make data
file list # [007] Check dog data # [008] Check cat data " ! " ‘_t_XXX.png’ &(Train) ‘_v_XXX.png’ (Validation) ‘_e_XXX.png’ %(Evaluate)
+.0 )C57 ,? %98 /- %98%:=>E04 @) H*(:[1, 0],
G*(:[0, 1] ,? ;F %98G or H OneHotVector )CA("$%&#!),? 6 2 %98'.(B1D ) numpy>E04<3
!#'% KerasOneHotVactor -*' &#$ import keras.utils as ku class_ids
= np.array([1, 0, 1, 1, 2, 0]) one_hots = ku.to_categorical(class_ids) print(one_hots) > [[0, 1, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 0, 0]] # [009] Make input and teacher data " (,cv2imread Numpy)+ OK
!$ Keras!$ 1) Sequential " 2) functional API# "
Keras!$ " 2) &( " 2)functional API# "'%
#&) % functional API$ % input_layer = Input((64,
64, 1)) cv_1_layer = Conv2D(32, 3)(input_layer) cv_2_layer = Conv2D(64, 3)(cv_1_layer) max_layer = MaxPooling2D()(cv_2_layer) flat_layer = Flatten()(max_layer) output_layer = Dense(2)(flat_layer) *'(" Keras !
Input&! input_layer = Input((64, 64, 1))
( Input(input_shape) #% input_shape ' $ "
Convolution!Conv2D1) Convolution! cv_1_layer = Conv2D(32, 3)(input_layer) 4 Conv2D(filters, kernel_size,
strides=(1, 1), padding=‘valid’, activation=None) *,- filters ( ) (30/%.-) kernel_size - 5 #"+% strides - 5 #"+% padding’valid’padding3 ’same’ 2('&$ activation
MaxPoolingMaxPooling2D.& MaxPooling max_layer = MaxPooling2D()(cv_layer) 3 MaxPooling2D(pool_size=(2, 2), strides=None)
')* pool_size *4 " (# strides *4 " (# ,%2+$ . /5610-!
Flatten# flat_layer = Flatten()(max_layer) % Flatten()
"! $&
Dense output_layer = Dense(2)(flat_layer)
Dense(units, activation=None) units activation
! Activate act_layer = Activate(activation='relu’)(cv_layer)
" Activate(activation=None) activation
;5F(%'- #! (%'- #!3D CB 14,"* (Input)!G.E7<,"* 9:input_shape==3A8 CB
@?& $!6>/ 0/2 & $!)+%'( @?)A8 3D # [010] Define Network
input_layer = Input(IMAGE_SHAPE) cv1_1 = Conv2D(32, 3, padding='same', input_shape=IMAGE_SHAPE)(input_layer)
cv1_1 = Activation(activation='relu')(cv1_1) cv1_2 = Conv2D(32, 3, padding='same')(cv1_1) cv1_2 = Activation(activation='relu')(cv1_2) cv1_max = MaxPooling2D()(cv1_2) cv2_1 = Conv2D(64, 3, padding='same')(cv1_max) cv2_1 = Activation(activation='relu')(cv2_1) cv2_2 = Conv2D(64, 3, padding='same')(cv2_1) cv2_2 = Activation(activation='relu')(cv2_2) cv2_max = MaxPooling2D()(cv2_2) flat_layer = Flatten()(cv2_max) fc = Dense(2)(flat_layer) output_layer = Activation(activation='softmax')(fc)
" Model# Keras model = Model(inputs=[input_layer],
outputs=[output_layer]) Model ! inputs ( ) outputs ( )( ) % $
()'# ! & '# model.summary() +*$ from keras.utils
import plot_model plot_model(model, to_file=MODEL_PNG_NAME) to_file " dot ()%
!- !- model.compile(optimizer=Adam(), loss='categorical_crossentropy',
metrics=['accuracy']) " %& optimizer loss metrics " [’accuracy’]( $ ,#' +*& crossentropy)
fit history = model.fit(train_inputs, train_teachers, batch_size=20,
epochs=100 , validation_data=(valid_inputs, valid_teachers) , shuffle=True, verbose=1, callbacks=callbacks) train_inputs$*#! train_teachers$*#') batch_size epochs % validation_data&(# (!, ')) shuffle! verbose$*!" (1 ) callbacks %()
+ "& + model.save_weights(save_file_name) $+ & ' )*%#(!
model.save(save_file_name) )* $+ & , &
+ # [012] Train
!*Adam )'$categorical_crossentropy & % (# +",
+./, # [013] Check loss and acc '
&#(3loss41acc41$ )% loss((3loss) % (3 )% val loss(*-loss) 5% "! 0(32
0, 0,&"#$ +( "#$ from keras.models import load_model model
= load_model(model_file) !%-. '/ $ model = Model(inputs=[input_layer], outputs=[output_layer]) model.load_weights(model_file) )'/ $ "#-.* $
predict model.predict(predict_inputs) predict_inputs (
) # [014] Load trained model # [015] Predict (Train data) # [016] Predict (Valid data) # [017] Predict (Evaluate data)
'43 51$ (-". 0 '43 , #!///+
)*//&2 1% //&2
,+#%3417 #%3417 0.68 !'" 5/ 2 3+-4+-Convolution&$* Dropout0.55/ .6
,+0.6 ()
Dropout drop_layer =
Dropout()(cv_layer) Dropout(rate) rate
;JIL0'$)8H "&*<F'$)G 8H"&*BK Keras Callback(!:= 64A;J-.
A;J1 lossE N28H</ Callback;J+C%#M(O@3 %#M) ) D97, ;J(fit)5?callbacls>?@3
# " ModelCheckpoint " import keras.callbacks as kc kc.ModelCheckpoint(
filepath=MODEL_FILE_NAME, save_weights_only=True , save_best_only=True, period=1) filepath ( ) save_weights_only save_best_only period ! val_loss!
AutoEncoder etc
…
Keras