LinearClassifier

• Domain: ai.onnx.ml

• Since version: 1

Linear classifier

Inputs

• X (T1): Data to be classified.

Outputs

• Y (T2): Classification outputs (one class per example).

• Z (tensor(float)): Classification scores ([N,E] - one score for each class and example

Type Constraints

• T1: The input must be a tensor of a numeric type, and of shape [N,C] or [C]. In the latter case, it will be treated as [1,C] Allowed types: tensor(double), tensor(float), tensor(int32), tensor(int64).

• T2: The output will be a tensor of strings or integers. Allowed types: tensor(int64), tensor(string).

Examples

test_cc_linearclassifier_int64_binary

Node:
  ai.onnx.ml.LinearClassifier(x) -> (y, z)
  Attributes:
    coefficients = [1.0, -1.0]
    intercepts = [0.0]
    multi_class = 0
    post_transform = "NONE"
    classlabels_ints = [0, 1]

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

Outputs:
  y: shape=(2,), dtype=int64
    [1, 0]
  z: shape=(2, 2), dtype=float32
    [[-1.,  1.],
     [ 3., -3.]]