Tensorflow stop and resume training
Your question very much depends on how you're augmenting your dataset. If your augmentation skews the statistical distribution of the underlying dataset then you should resume training with the pre-power outage dataset. Otherwise, you're assuming that your augmentation has not changed the distribution of the dataset.
It is a fairly safe assumption to make (assuming your augmentations do not change the data in an extremely significant way) that you are safe to resume training on a new dataset or the old dataset without significant change in accuracy.
I am using Tensorflow to train my model. I am routinely saving my model every 10 epochs. I have a limited number of samples to train, so I am augmenting my dataset to make a larger training dataset.
If I need to use my saved model to resume training after a power outage would it be best to resume training using the same dataset or to make a new dataset?