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: Defuse the fused activation layers in the network.
          
docstr_tok: ["Defuse","the","fused","activation","layers","in","the","network","."]
code: def defuse_activation(self):
      """Defuse the fused activation layers in the network."""
      idx, nb_layers = 0, len(self.layer_list)
      while idx < nb_layers:
          layer = self.layer_list[idx]
          k_layer = self.keras_layer_map[layer]
          if isinstance(k_layer, _keras.layers.TimeDistributed):
              k_layer = k_layer.layer
          if (
              isinstance(k_layer, _keras.layers.Conv2D)
              or isinstance(k_layer, _keras.layers.Conv1D)
              or isinstance(k_layer, _keras.layers.SeparableConv2D)
              or isinstance(k_layer, _keras.layers.SeparableConv1D)
              or isinstance(k_layer, _keras.layers.Dense)
          ):
              func_name = (
                  k_layer.activation.func_name
                  if six.PY2
                  else k_layer.activation.__name__
              )
  
              if func_name != "linear":
                  # Create new layer
                  new_layer = layer + "__activation__"
                  new_keras_layer = _keras.layers.core.Activation(
                      func_name
                  )
                  # insert new layer after it
                  self._insert_layer_after(
                      idx, new_layer, new_keras_layer
                  )
                  idx += 1
                  nb_layers += 1
          idx += 1
  
imports: import keras
  import numpy
  import six
  from distutils.version import StrictVersion
label_desc: Use image node_id 30 for calling the NetGraph obj's underlying member method code with example usage: obj.defuse_activation() without return types