Why can't I use data augmentation with a pretrained convnet? Reading Deep Learning with Python by François Chollet. In section 5.3.1, we've instantiated a pretrained convnet, VGG16, and are given two options to proceed:


  A) Running the convolutional base over your dataset, recording its
  output to a Numpy array on disk, and then using this data as input to
  a standalone, densely connected classifier similar to those you saw in
  part 1 of this book. This solution is fast and cheap to run, because
  it only requires running the convolutional base once for every input
  image, and the convolutional base is by far the most expensive part of
  the pipeline. But for the same reason, this technique won’t allow you
  to use data augmentation.
  
  B) Extending the model you have (conv_base) by adding Dense layers on
  top, and running the whole thing end to end on the input data. This
  will allow you to use data augmentation, because every input image
  goes through the convolutional base every time it’s seen by the model.
  But for the same reason, this technique is far more expensive than the
  first.


Why can't I use data augmentation to generate more training data from existing training samples then go with option A? Seems like I can run the VGG16 base over my augmented dataset and use the output as the input to a standalone densely connected classifier.
Found the answer in stats.stackexchange.com. Hopefully this helps anyone else with the same question.

feature extraction: freezing convolutional base vs. training on extracted features