.. _l-onnx-docai.onnx.ml-LinearClassifier: ============================= ai.onnx.ml - LinearClassifier ============================= .. contents:: :local: .. _l-onnx-opai-onnx-ml-linearclassifier-1: 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**: 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.
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 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**