ai.onnx.ml - LinearClassifier

LinearClassifier - 1 (ai.onnx.ml)

Version

• name: LinearClassifier (GitHub)

• domain: ai.onnx.ml

• since_version: 1

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 1 of domain ai.onnx.ml.

Summary

Linear classifier

Attributes

• classlabels_ints - INTS :

  Class labels when using integer labels. One and only one ‘classlabels’ attribute must be defined.

• classlabels_strings - STRINGS :

  Class labels when using string labels. One and only one ‘classlabels’ attribute must be defined.

• coefficients - FLOATS (required) :

  A collection of weights of the model(s).

• intercepts - FLOATS :

  A collection of intercepts.

• multi_class - INT (default is '0'):

  Indicates whether to do OvR or multinomial (0=OvR is the default).

• post_transform - STRING (default is 'NONE'):

  Indicates the transform to apply to the scores vector.
  One of ‘NONE,’ ‘SOFTMAX,’ ‘LOGISTIC,’ ‘SOFTMAX_ZERO,’ or ‘PROBIT’

Inputs

• X (heterogeneous) - T1:

  Data to be classified.

Outputs

• Y (heterogeneous) - T2:

  Classification outputs (one class per example).

• Z (heterogeneous) - tensor(float):

  Classification scores ([N,E] - one score for each class and example

Type Constraints

• T1 in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ):

  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]

• T2 in ( tensor(int64), tensor(string) ):

  The output will be a tensor of strings or integers.