class_docstr: Attributes:
  layer_list - a list of layer names in the Keras model
  connection_map - a map where the key is a layer, the value is a list of its successors
  reverse_connection_map - a map where the key is a layer, the value is a list of its predecessors
  keras_layer_map - a map where the key is a layer name, the value is Keras layer
  model - a reference of the keras model.
  blob_names - blob names for each one of the edge.
class_docstr_tok: ["Attributes",":","layer_list","-","a","list","of","layer","names","in","the","Keras","model","connection_map","-","a","map","where","the","key","is","a","layer",",","the","value","is","a","list","of","its","successors","reverse_connection_map","-","a","map","where","the","key","is","a","layer",",","the","value","is","a","list","of","its","predecessors","keras_layer_map","-","a","map","where","the","key","is","a","layer","name",",","the","value","is","Keras","layer","model","-","a","reference","of","the","keras","model",".","blob_names","-","blob","names","for","each","one","of","the","edge","."]
docstr: Extract the ordering of output layers.
docstr_tok: ["Extract","the","ordering","of","output","layers","."]
code: def make_output_layers(self):
      """
      Extract the ordering of output layers.
      """
      # TODO
      # use successors == 0 as the criteria for output layer
      # will fail when some intermediate layers also generate output.
      # However, because the possibility of having inserted layers,
      # it's more difficult to tell which layer is the output layer.
      # Once possible way is to keep track of newly added layers...
      self.output_layers = []
      for layer in self.layer_list:
          if len(self.get_successors(layer)) == 0:
              self.output_layers.append(layer)
  
imports: import keras
  import numpy
label_desc: Use image node_id 7 for calling the NetGraph obj's underlying member method code with example usage: obj.make_output_layers() without return types