module onnxrt.ops_cpu.op_linear_classifier

Short summary

module mlprodict.onnxrt.ops_cpu.op_linear_classifier

Runtime operator.

source on GitHub

Classes

class	truncated documentation
LinearClassifier	LinearClassifier (ai.onnx.ml) ============================= Linear classifier Attributes

Properties

property	truncated documentation
args_default	Returns the list of arguments as well as the list of parameters with the default values (close to the signature). …
args_default_modified	Returns the list of modified parameters.
args_mandatory	Returns the list of optional arguments.
args_optional	Returns the list of optional arguments.
atts_value	Returns all parameters in a dictionary.
nb_classes	Returns the number of expected classes.

Methods

method	truncated documentation
__init__
_run

Documentation

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_linear_classifier.LinearClassifier(ai.onnx.ml)

Bases: OpRunClassifierProb, _ClassifierCommon

---

Linear classifier

Attributes

• classlabels_ints: Class labels when using integer labels. One and only one ‘classlabels’ attribute must be defined. default value cannot be automatically retrieved (INTS)

• classlabels_strings: Class labels when using string labels. One and only one ‘classlabels’ attribute must be defined. default value cannot be automatically retrieved (STRINGS)

• coefficients (required): A collection of weights of the model(s). default value cannot be automatically retrieved (FLOATS)

• intercepts: A collection of intercepts. default value cannot be automatically retrieved (FLOATS)

• multi_class: Indicates whether to do OvR or multinomial (0=OvR is the default). Default value is namemulticlassi0typeINT (INT)

• post_transform: Indicates the transform to apply to the scores vector. One of ‘NONE,’ ‘SOFTMAX,’ ‘LOGISTIC,’ ‘SOFTMAX_ZERO,’ or ‘PROBIT’ Default value is nameposttransformsNONEtypeSTRING (STRING)

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 tensor(float), tensor(double), tensor(int64), tensor(int32): 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 tensor(string), tensor(int64): The output will be a tensor of strings or integers.

Version

Onnx name: LinearClassifier

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

Runtime implementation: LinearClassifier

__init__(onnx_node, desc=None, **options)

_run(x, attributes=None, verbose=0, fLOG=None)

Should be overwritten.

source on GitHub