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'<9
A'< 
 D+('24)
(6. 8) FBC;&C'<
-= (@+#E) 

Keras PythonA 8A=?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("$%&#!),? 62 %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  '  &#(3loss41
acc41$  )%
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  
"&*?@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