module `onnxrt.ops_cpu.op_linear_classifier`#

Short summary#

module mlprodict.onnxrt.ops_cpu.op_linear_classifier

Runtime operator.

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

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.

method	truncated documentation
`__init__`
`_run`

Runtime operator.

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

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

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 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

Parameters

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

Parameters

_run(x)#

Should be overwritten.

module onnxrt.ops_cpu.op_less

module onnxrt.ops_cpu.op_linear_regressor