TensorFlow 2.0 - Normalizing input to DNN (on structured data) I have a structured dataset of around 100 gigs, and I am using DNN for classification in TF 2.0. Because of this huge dataset, I cannot load entire data in memory for training. So, I'll be reading data in batches to train the model.

Now, the input to the network should be normalized and for that, I need training dataset mean and SD. I have been reading TensorFlow docs to get info on how to normalize features when reading data in batches. But, couldn't find one. though I found this article, it is only for the case where entire data can be loaded in memory.

So, If any of you have worked on creating such a TensorFlow data pipeline for normalizing input features while loading data in batches and training model, It would be helpful.
One way could be to first iterate over the dataset in batches, just to get the mean and sd. Then when running training, use the true population parameters obtained before.