ThresholdedRelu

ThresholdedRelu - 22

Version

  • name: ThresholdedRelu (GitHub)

  • domain: main

  • since_version: 22

  • function: True

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 22.

Summary

ThresholdedRelu takes one input data (Tensor) and produces one output data (Tensor) where the rectified linear function, y = x for x > alpha, y = 0 otherwise, is applied to the tensor elementwise.

Function Body

The function definition for this operator.

< domain: "", opset_import: ["" : 18] > ThresholdedRelu <alpha>(X) => (Y) { Alpha = Constant <value_float: float = @alpha> () AlphaCast = CastLike (Alpha, X) Zero = Constant <value: tensor = float {0}> () ZeroCast = CastLike (Zero, X) AlphaLessThanX = Less (AlphaCast, X) Y = Where (AlphaLessThanX, X, ZeroCast) }

Attributes

  • alpha - FLOAT (default is '1.0'):

    Threshold value

Inputs

  • X (heterogeneous) - T:

    Input tensor

Outputs

  • Y (heterogeneous) - T:

    Output tensor

Type Constraints

  • T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16) ):

    Constrain input and output types to float tensors.

ThresholdedRelu - 10

Version

  • name: ThresholdedRelu (GitHub)

  • domain: main

  • since_version: 10

  • function: True

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 10.

Summary

ThresholdedRelu takes one input data (Tensor) and produces one output data (Tensor) where the rectified linear function, y = x for x > alpha, y = 0 otherwise, is applied to the tensor elementwise.

Function Body

The function definition for this operator.

< domain: "", opset_import: ["" : 18] > ThresholdedRelu <alpha>(X) => (Y) { Alpha = Constant <value_float: float = @alpha> () AlphaCast = CastLike (Alpha, X) Zero = Constant <value: tensor = float {0}> () ZeroCast = CastLike (Zero, X) AlphaLessThanX = Less (AlphaCast, X) Y = Where (AlphaLessThanX, X, ZeroCast) }

Attributes

  • alpha - FLOAT (default is '1.0'):

    Threshold value

Inputs

  • X (heterogeneous) - T:

    Input tensor

Outputs

  • Y (heterogeneous) - T:

    Output tensor

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16) ):

    Constrain input and output types to float tensors.