message: Standardize references in NASNet
  Add cautions to Large/Mobile implementations
diff: @@ -33,9 +33,9 @@ The below table describes the performance on ImageNet 2012:
  | NASNet-A (6 @ 4032) | 82.7 % | 96.2 % | 23.8 B | 88.9 |
  --------------------------------------------------------------------------------
  -Reference paper:
  - - [Learning Transferable Architectures for Scalable Image Recognition]
  - (https://arxiv.org/abs/1707.07012) (CVPR 2018)
  +Reference:
  + - [Learning Transferable Architectures for Scalable Image Recognition](
  + https://arxiv.org/abs/1707.07012) (CVPR 2018)
  """
  from __future__ import absolute_import
  from __future__ import division
  @@ -336,6 +336,9 @@ def NASNetMobile(input_shape=None,
  Note that the data format convention used by the model is
  the one specified in your Keras config at `~/.keras/keras.json`.
  + Caution: Be sure to properly pre-process your inputs to the application.
  + Please see `applications.nasnet.preprocess_input` for an example.
  +
  Arguments:
  input_shape: Optional shape tuple, only to be specified
  if `include_top` is False (otherwise the input shape
  @@ -404,6 +407,9 @@ def NASNetLarge(input_shape=None,
  Note that the data format convention used by the model is
  the one specified in your Keras config at `~/.keras/keras.json`.
  + Caution: Be sure to properly pre-process your inputs to the application.
  + Please see `applications.nasnet.preprocess_input` for an example.
  +
  Arguments:
  input_shape: Optional shape tuple, only to be specified
  if `include_top` is False (otherwise the input shape
  