We propose a restricted class of tensor network state, built from number-state preserving tensors, for supervised learning tasks. This is argued to be natural choice classifiers as 1) they map classical data data, and thus preserve the interpretability under transformations, 2) can efficiently trained maximize their scalar product against sets, 3) seem powerful generic (unrestricted) networks i...