Additional minor edits to the CIFAR tutorial.
"\n",
"In this notebook, we'll demonstrate the steps necessary to train a medium sized DenseNet (https://arxiv.org/abs/1608.06993) on either of two popularly used benchmark dataset in computer vision (CIFAR10 and CIFAR100). We'll be training the DKL model entirely end to end using the standard 300 Epoch training schedule and SGD.\n",
"\n",
- "This notebook is largely for tutorial purposes. If your goal is just to get (for example) a trained DKL + CIFAR100 model, we __recommend__ that you move this code to a simple python script and run that, rather than training directly out of a python notebook. We find that training is just a bit faster out of a python notebook.\n",
+ "This notebook is largely for tutorial purposes. If your goal is just to get (for example) a trained DKL + CIFAR100 model, we __recommend__ that you move this code to a simple python script and run that, rather than training directly out of a python notebook. We find that training is just a bit faster out of a python notebook. We also of course recommend that you increase the size of the DenseNet used to a full sized model if you would like to achieve state of the art performance.\n",
"\n",
"Furthermore, because this notebook involves training an actually reasonably large neural network, it is __strongly recommended__ that you have a decent GPU available for this, as with all large deep learning models."
]
