- Normalizer#

Normalizer - 1 (


  • name: Normalizer (GitHub)

  • domain:

  • since_version: 1

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: False

This version of the operator has been available since version 1 of domain


Normalize the input. There are three normalization modes, which have the corresponding formulas, defined using element-wise infix operators ‘/’ and ‘^’ and tensor-wide functions ‘max’ and ‘sum’:

Max: Y = X / max(X)

L1: Y = X / sum(X)

L2: Y = sqrt(X^2 / sum(X^2)}

In all modes, if the divisor is zero, Y == X.

For batches, that is, [N,C] tensors, normalization is done along the C axis. In other words, each row of the batch is normalized independently.


  • norm: One of ‘MAX,’ ‘L1,’ ‘L2’ Default value is 'MAX'.


  • X (heterogeneous) - T: Data to be encoded, a tensor of shape [N,C] or [C]


  • Y (heterogeneous) - tensor(float): Encoded output data

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ): The input must be a tensor of a numeric type.