I’d like to use nn.BatchNorm()
in front of an auto-encoder model to standardize the input. So far standardization is done by an external sklearn StandardScaler()
and I’d like to avoid having to persist 2 artifacts (scaler + autoencoder). Is there something specific to do for the gluon.nn.BatchNorm()
to be used at inference time?