original_docstring: 
          Builds a simple One_vs_One_Inception network with 2 inception layers on the top of the current model
           (useful for ECOC_loss models).
          
docstring: Builds a simple One_vs_One_Inception network with 2 inception layers on the top of the current model
  (useful for ECOC_loss models).
docstring_tokens: Builds a simple One_vs_One_Inception network with 2 inception layers on the top of the current model ( useful for ECOC_loss models ) .
code: def add_One_vs_One_Inception_Functional(self, input_layer, input_shape, id_branch, nOutput=2, activation='softmax'):
          """
          Builds a simple One_vs_One_Inception network with 2 inception layers on the top of the current model
           (useful for ECOC_loss models).
          """
  
          in_node = self.model.get_layer(input_layer).output
  
          # Inception Ea
          [out_Ea, out_Ea_name] = self.__addInception_Functional('inceptionEa_' + str(id_branch), in_node, 4, 2, 8, 2, 2,
                                                                 2)
          # Inception Eb
          [out_Eb, out_Eb_name] = self.__addInception_Functional('inceptionEb_' + str(id_branch), out_Ea, 2, 2, 4, 2, 1,
                                                                 1)
          # Average Pooling    pool_size=(7,7)
          x = AveragePooling2D(pool_size=input_shape, strides=(1, 1), name='ave_pool/ECOC_' + str(id_branch))(out_Eb)
  
          # Softmax
          output_name = 'fc_OnevsOne_' + str(id_branch)
          x = Flatten(name='fc_OnevsOne_' + str(id_branch) + '/flatten')(x)
          x = Dropout(0.5, name='fc_OnevsOne_' + str(id_branch) + '/drop')(x)
          out_node = Dense(nOutput, activation=activation, name=output_name)(x)
  
          return out_node
code_tokens: def add_One_vs_One_Inception_Functional ( self , input_layer , input_shape , id_branch , nOutput = 2 , activation = 'softmax' ) : in_node = self . model . get_layer ( input_layer ) . output [ out_Ea , out_Ea_name ] = self . __addInception_Functional ( 'inceptionEa_' + str ( id_branch ) , in_node , 4 , 2 , 8 , 2 , 2 , 2 ) [ out_Eb , out_Eb_name ] = self . __addInception_Functional ( 'inceptionEb_' + str ( id_branch ) , out_Ea , 2 , 2 , 4 , 2 , 1 , 1 ) x = AveragePooling2D ( pool_size = input_shape , strides = ( 1 , 1 ) , name = 'ave_pool/ECOC_' + str ( id_branch ) ) ( out_Eb ) output_name = 'fc_OnevsOne_' + str ( id_branch ) x = Flatten ( name = 'fc_OnevsOne_' + str ( id_branch ) + '/flatten' ) ( x ) x = Dropout ( 0.5 , name = 'fc_OnevsOne_' + str ( id_branch ) + '/drop' ) ( x ) out_node = Dense ( nOutput , activation = activation , name = output_name ) ( x ) return out_node
short_docstring: Builds a simple One_vs_One_Inception network with 2 inception layers on the top of the current model
  (useful for ECOC_loss models).
short_docstring_tokens: Builds a simple One_vs_One_Inception network with 2 inception layers on the top of the current model ( useful for ECOC_loss models ) .
comment: """
          Builds a simple One_vs_One_Inception network with 2 inception layers on the top of the current model
           (useful for ECOC_loss models).
          """$SEP$# Inception Ea$SEP$# Inception Eb$SEP$# Average Pooling    pool_size=(7,7)$SEP$# Softmax