class_docstr: An Xception hypermodel.
  
  Models built by `HyperXception` take images with shape (height, width,
  channels) as input. The output are one-hot encoded with the length matching
  the number of classes specified by the `classes` argument.
  
  Args:
      include_top: Boolean, whether to include the fully-connected layer at
          the top of the network.
      input_shape: Optional shape tuple, e.g. `(256, 256, 3)`.  One of
          `input_shape` or `input_tensor` must be specified.
      input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`)
          to use as image input for the model.  One of `input_shape` or
          `input_tensor` must be specified.
      classes: Optional number of classes to classify images into, only to be
          specified if `include_top` is True, and if no `weights` argument is
          specified.
      **kwargs: Additional keyword arguments that apply to all hypermodels.
          See `keras_tuner.HyperModel`.
class_docstr_tok: ["An","Xception","hypermodel",".","Models","built","by","`","HyperXception","`","take","images","with","shape","(","height",",","width",",","channels",")","as","input",".","The","output","are","one-hot","encoded","with","the","length","matching","the","number","of","classes","specified","by","the","`","classes","`","argument",".","Args",":","include_top",":","Boolean",",","whether","to","include","the","fully-connected","layer","at","the","top","of","the","network",".","input_shape",":","Optional","shape","tuple",",","e.g",".","`","(","256",",","256",",","3",")","`",".","One","of","`","input_shape","`","or","`","input_tensor","`","must","be","specified",".","input_tensor",":","Optional","Keras","tensor","(","i.e",".","output","of","`","layers.Input","(",")","`",")","to","use","as","image","input","for","the","model",".","One","of","`","input_shape","`","or","`","input_tensor","`","must","be","specified",".","classes",":","Optional","number","of","classes","to","classify","images","into",",","only","to","be","specified","if","`","include_top","`","is","True",",","and","if","no","`","weights","`","argument","is","specified",".","*","*","kwargs",":","Additional","keyword","arguments","that","apply","to","all","hypermodels",".","See","`","keras_tuner.HyperModel","`","."]
code: def __init__(
      self,
      include_top=True,
      input_shape=None,
      input_tensor=None,
      classes=None,
      **kwargs,
  ):
      super().__init__(**kwargs)
      if include_top and classes is None:
          raise ValueError(
              "You must specify `classes` when `include_top=True`"
          )
  
      if input_shape is None and input_tensor is None:
          raise ValueError(
              "You must specify either `input_shape` or `input_tensor`."
          )
  
      self.include_top = include_top
      self.input_shape = input_shape
      self.input_tensor = input_tensor
      self.classes = classes
  
file_path: keras-tuner/keras_tuner/applications/xception.py
imports: from keras_tuner.api_export import keras_tuner_export
  from keras_tuner.backend import keras
  from keras_tuner.backend import ops
  from keras_tuner.backend.keras import layers
  from keras_tuner.engine import hypermodel
label_desc: Use image node_id 1 to create a new HyperXception object from inherited base classes: hypermodel with example: obj = HyperXception(include_top, input_shape, input_tensor, classes)