it’s more natural to treat your dataset as 10 channels and 1 feature. This is because over the next few layers, convolutions will actually try to learn correlated transforms between data projections.
For BatchNorm, normalizing channel-wise is much more natural, because you dont want to normalize from one feature to another. (each feature can have different ranges, etc.).