ai.onnx.ml - LinearClassifier#
LinearClassifier - 1 (ai.onnx.ml)#
Version
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: Class labels when using integer labels. One and only one ‘classlabels’ attribute must be defined.
classlabels_strings: Class labels when using string labels. One and only one ‘classlabels’ attribute must be defined.
coefficients (required): A collection of weights of the model(s).
intercepts: A collection of intercepts.
multi_class: Indicates whether to do OvR or multinomial (0=OvR is the default). Default value is
0.post_transform: Indicates the transform to apply to the scores vector.<br>One of ‘NONE,’ ‘SOFTMAX,’ ‘LOGISTIC,’ ‘SOFTMAX_ZERO,’ or ‘PROBIT’ Default value is
'NONE'.
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 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.
Examples