ai.onnx.ml - Normalizer#
Normalizer - 1 (ai.onnx.ml)#
Version
name: Normalizer (GitHub)
domain: ai.onnx.ml
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 ai.onnx.ml.
Summary
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.
Attributes
norm: One of ‘MAX,’ ‘L1,’ ‘L2’ Default value is
'MAX'.
Inputs
X (heterogeneous) - T: Data to be encoded, a tensor of shape [N,C] or [C]
Outputs
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.
Examples