Although the loss is one value, it represents the sum of losses over all examples in the batch, hence the effective total loss is the sum of losses over all N images rather than sum over M batches. The normalization values is mean and std values, it differs for various datasets, however, 0.5 is generally a good default to use for any generic dataset.