Normalizer#

Domain: ai.onnx.ml
Since version: 1

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.

Inputs

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

Outputs

Y (tensor(float)): Encoded output data

Type Constraints

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

Examples#

test_cc_normalizer_l1_int64

Node:
  ai.onnx.ml.Normalizer(x) -> (y)
  Attributes:
    norm = "L1"

Inputs:
  x: shape=(4,), dtype=int64
    [ 1, -1,  2, -2]

Outputs:
  y: shape=(4,), dtype=float32
    [ 0.16666667, -0.16666667,  0.33333334, -0.33333334]

test_cc_normalizer_l2_float

Node:
  ai.onnx.ml.Normalizer(x) -> (y)
  Attributes:
    norm = "L2"

Inputs:
  x: shape=(2, 3), dtype=float32
    [[1., 2., 2.],
     [0., 3., 4.]]

Outputs:
  y: shape=(2, 3), dtype=float32
    [[0.33333334, 0.6666667 , 0.6666667 ],
     [0.        , 0.6       , 0.8       ]]

test_cc_normalizer_max_double

Node:
  ai.onnx.ml.Normalizer(x) -> (y)
  Attributes:
    norm = "MAX"

Inputs:
  x: shape=(2, 3), dtype=float64
    [[ 1., -3.,  2.],
     [ 0.,  0.,  0.]]

Outputs:
  y: shape=(2, 3), dtype=float32
    [[ 0.5, -1.5,  1. ],
     [ 0. ,  0. ,  0. ]]