original_docstring: Base network to be shared (eq. to feature extraction).
      
docstring: Base network to be shared (eq. to feature extraction).
docstring_tokens: Base network to be shared ( eq . to feature extraction ) .
code: def create_base_network(input_shape):
      '''Base network to be shared (eq. to feature extraction).
      '''
      model = Sequential()
      model.add(Conv2D(32, kernel_size=(3, 3),
                       activation='relu',
                       input_shape=input_shape))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Conv2D(128, (3, 3), activation='relu'))
      model.add(Conv2D(256, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.25))
      model.add(Flatten())
      model.add(Dense(128, activation='relu'))
      #model.add(Dropout(0.5))
      #model.add(Dense(128, activation='relu'))
      return model
code_tokens: def create_base_network ( input_shape ) : model = Sequential ( ) model . add ( Conv2D ( 32 , kernel_size = ( 3 , 3 ) , activation = 'relu' , input_shape = input_shape ) ) model . add ( Conv2D ( 64 , ( 3 , 3 ) , activation = 'relu' ) ) model . add ( MaxPooling2D ( pool_size = ( 2 , 2 ) ) ) model . add ( Conv2D ( 128 , ( 3 , 3 ) , activation = 'relu' ) ) model . add ( Conv2D ( 256 , ( 3 , 3 ) , activation = 'relu' ) ) model . add ( MaxPooling2D ( pool_size = ( 2 , 2 ) ) ) model . add ( Dropout ( 0.25 ) ) model . add ( Flatten ( ) ) model . add ( Dense ( 128 , activation = 'relu' ) ) return model
short_docstring: Base network to be shared (eq.
short_docstring_tokens: Base network to be shared ( eq .
comment: '''Base network to be shared (eq. to feature extraction).
      '''$SEP$#model.add(Dropout(0.5))$SEP$#model.add(Dense(128, activation='relu'))