message: Update docstring for tf.keras.applications.InceptionV3.
diff: @@ -52,42 +52,46 @@ def InceptionV3(include_top=True,
  classes=1000):
  """Instantiates the Inception v3 architecture.
  + Reference paper:
  + - [Rethinking the Inception Architecture for Computer Vision](
  + http://arxiv.org/abs/1512.00567) (CVPR 2016)
  +
  Optionally loads weights pre-trained on ImageNet.
  Note that the data format convention used by the model is
  - the one specified in your Keras config at `~/.keras/keras.json`.
  + the one specified in the `tf.keras.backend.image_data_format()`.
  Arguments:
  - include_top: whether to include the fully-connected
  - layer at the top of the network.
  - weights: one of `None` (random initialization),
  - 'imagenet' (pre-training on ImageNet),
  - or the path to the weights file to be loaded.
  - input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
  - to use as image input for the model.
  - input_shape: optional shape tuple, only to be specified
  + include_top: Boolean, whether to include the fully-connected
  + layer at the top, as the last layer of the network. Default to `True`.
  + weights: One of `None` (random initialization),
  + `imagenet` (pre-training on ImageNet),
  + or the path to the weights file to be loaded. Default to `imagenet`.
  + input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`)
  + to use as image input for the model. `input_tensor` is useful for sharing
  + inputs between multiple different networks. Default to None.
  + input_shape: Optional shape tuple, only to be specified
  if `include_top` is False (otherwise the input shape
  has to be `(299, 299, 3)` (with `channels_last` data format)
  or `(3, 299, 299)` (with `channels_first` data format).
  It should have exactly 3 inputs channels,
  and width and height should be no smaller than 75.
  E.g. `(150, 150, 3)` would be one valid value.
  + `input_shape` will be ignored if the `input_tensor` is provided.
  pooling: Optional pooling mode for feature extraction
  when `include_top` is `False`.
  - - `None` means that the output of the model will be
  - the 4D tensor output of the
  - last convolutional block.
  + - `None` (default) means that the output of the model will be
  + the 4D tensor output of the last convolutional block.
  - `avg` means that global average pooling
  will be applied to the output of the
  last convolutional block, and thus
  the output of the model will be a 2D tensor.
  - - `max` means that global max pooling will
  - be applied.
  + - `max` means that global max pooling will be applied.
  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.
  + if no `weights` argument is specified. Default to 1000.
  Returns:
  - A Keras model instance.
  + A Keras `tf.keras.Model` instance.
  Raises:
  ValueError: in case of invalid argument for `weights`,
  