original_docstring: 
              Builds a simple One_vs_One_Inception_v2 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_v2 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_v2 network with 2 inception layers on the top of the current model ( useful for ECOC_loss models ) .
code: def add_One_vs_One_Inception_v2(self, input_layer, input_shape, id_branch, nOutput=2, activation='softmax'):
          """
              Builds a simple One_vs_One_Inception_v2 network with 2 inception layers on the top of the current model
              (useful for ECOC_loss models).
          """
  
          # Inception Ea
          out_Ea = self.__addInception('inceptionEa_' + str(id_branch), input_layer, 16, 8, 32, 8, 8, 8)
          # Inception Eb
          out_Eb = self.__addInception('inceptionEb_' + str(id_branch), out_Ea, 8, 8, 16, 8, 4, 4)
          # Average Pooling    pool_size=(7,7)
          self.model.add_node(AveragePooling2D(pool_size=input_shape[1:], strides=(1, 1)),
                              name='ave_pool/ECOC_' + str(id_branch), input=out_Eb)
          # Softmax
          self.model.add_node(Flatten(),
                              name='fc_OnevsOne_' + str(id_branch) + '/flatten', input='ave_pool/ECOC_' + str(id_branch))
          self.model.add_node(Dropout(0.5),
                              name='fc_OnevsOne_' + str(id_branch) + '/drop',
                              input='fc_OnevsOne_' + str(id_branch) + '/flatten')
          output_name = 'fc_OnevsOne_' + str(id_branch)
          self.model.add_node(Dense(nOutput, activation=activation),
                              name=output_name, input='fc_OnevsOne_' + str(id_branch) + '/drop')
  
          return output_name
code_tokens: def add_One_vs_One_Inception_v2 ( self , input_layer , input_shape , id_branch , nOutput = 2 , activation = 'softmax' ) : out_Ea = self . __addInception ( 'inceptionEa_' + str ( id_branch ) , input_layer , 16 , 8 , 32 , 8 , 8 , 8 ) out_Eb = self . __addInception ( 'inceptionEb_' + str ( id_branch ) , out_Ea , 8 , 8 , 16 , 8 , 4 , 4 ) self . model . add_node ( AveragePooling2D ( pool_size = input_shape [ 1 : ] , strides = ( 1 , 1 ) ) , name = 'ave_pool/ECOC_' + str ( id_branch ) , input = out_Eb ) self . model . add_node ( Flatten ( ) , name = 'fc_OnevsOne_' + str ( id_branch ) + '/flatten' , input = 'ave_pool/ECOC_' + str ( id_branch ) ) self . model . add_node ( Dropout ( 0.5 ) , name = 'fc_OnevsOne_' + str ( id_branch ) + '/drop' , input = 'fc_OnevsOne_' + str ( id_branch ) + '/flatten' ) output_name = 'fc_OnevsOne_' + str ( id_branch ) self . model . add_node ( Dense ( nOutput , activation = activation ) , name = output_name , input = 'fc_OnevsOne_' + str ( id_branch ) + '/drop' ) return output_name
short_docstring: Builds a simple One_vs_One_Inception_v2 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_v2 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_v2 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