When using Batch Normalization we can use the batch mean and batch variance during training.However this fails to give good enough results in validation case. We get the best results by providing the population mean and population variance.However this means that the two graphs will be almost identical except the part where we need to subtract the mean and divide the standard deviation (we use the population ones)Our current implementation worked ok but it includes too much boilerplate code.Note that in other frameworks you can extract and manipulate layers of a model quite easily for such cases, but tensorflow has an only append policy to its graphsWhat is the optimal way to go in such cases where one graph is slightly different from the other?
What is the right way to implement the graph of validation process of Batch Normalization in tensorflow?
tensorflow;normalization