Hi everybody,
I am currently trying to train a regressor that accepts 5 dimensional features an outputs a single value. The neural network architecture that I used accepts batch of input data and I use batchNorm in the first layer.
But beacuse of the nature of batchNorm network generates normalized predictions. For evaluation stage ground truth values should be normalized in order to make a comparison. To do that I used a single batchNorm layer that only generates normalized values of batches with the same size that the network has been fed.
My question is that; is this approach valid or not?
Thanks.
Can.