PRelu#

PRelu - 16#

Version#

  • name: PRelu (GitHub)

  • domain: main

  • since_version: 16

  • function: True

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 16.

Summary#

PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function f(x) = slope * x for x < 0, f(x) = x for x >= 0., is applied to the data tensor elementwise. This operator supports unidirectional broadcasting (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check Broadcasting in ONNX.

Function Body#

The function definition for this operator.

<
  domain: "",
  opset_import: ["" : 16]
>
PRelu (X, slope) => (Y)
{
   Zero = Constant <value: tensor = float {0}> ()
   ZeroCast = CastLike (Zero, X)
   XLessThanZero = Less (X, ZeroCast)
   SlopeMulX = Mul (slope, X)
   Y = Where (XLessThanZero, SlopeMulX, X)
}

Inputs#

  • X (heterogeneous) - T:

    Input tensor

  • slope (heterogeneous) - T:

    Slope tensor. The shape of slope can be smaller than first input X; if so, its shape must be unidirectional broadcastable to X

Outputs#

  • Y (heterogeneous) - T:

    Output tensor (same size as X)

Type Constraints#

  • T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64) ):

    Constrain input and output types to float/int tensors.

PRelu - 9#

Version#

  • name: PRelu (GitHub)

  • domain: main

  • since_version: 9

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 9.

Summary#

PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function f(x) = slope * x for x < 0, f(x) = x for x >= 0., is applied to the data tensor elementwise. This operator supports unidirectional broadcasting (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check Broadcasting in ONNX.

Inputs#

  • X (heterogeneous) - T:

    Input tensor

  • slope (heterogeneous) - T:

    Slope tensor. The shape of slope can be smaller than first input X; if so, its shape must be unidirectional broadcastable to X

Outputs#

  • Y (heterogeneous) - T:

    Output tensor (same size as X)

Type Constraints#

  • T in ( tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64) ):

    Constrain input and output types to float/int tensors.

PRelu - 7#

Version#

  • name: PRelu (GitHub)

  • domain: main

  • since_version: 7

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 7.

Summary#

PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function f(x) = slope * x for x < 0, f(x) = x for x >= 0., is applied to the data tensor elementwise. This operator supports unidirectional broadcasting (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check Broadcasting in ONNX.

Inputs#

  • X (heterogeneous) - T:

    Input tensor

  • slope (heterogeneous) - T:

    Slope tensor. The shape of slope can be smaller than first input X; if so, its shape must be unidirectional broadcastable to X

Outputs#

  • Y (heterogeneous) - T:

    Output tensor (same size as X)

Type Constraints#

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

    Constrain input and output types to float tensors.

PRelu - 6#

Version#

  • name: PRelu (GitHub)

  • domain: main

  • since_version: 6

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 6.

Summary#

PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function f(x) = slope * x for x < 0, f(x) = x for x >= 0., is applied to the data tensor elementwise.

Inputs#

  • X (heterogeneous) - T:

    Input tensor

  • slope (heterogeneous) - T:

    Slope tensor. If Slope is of size 1, the value is sharedacross different channels

Outputs#

  • Y (heterogeneous) - T:

    Output tensor

Type Constraints#

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

    Constrain input and output types to float tensors.

PRelu - 1#

Version#

  • name: PRelu (GitHub)

  • domain: main

  • since_version: 1

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: False

This version of the operator has been available since version 1.

Summary#

PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function f(x) = slope * x for x < 0, f(x) = x for x >= 0., is applied to the data tensor elementwise.

Attributes#

  • consumed_inputs - INTS :

    legacy optimization attribute.

Inputs#

  • X (heterogeneous) - T:

    Input tensor

  • slope (heterogeneous) - T:

    Slope tensor. If Slope is of size 1, the value is sharedacross different channels

Outputs#

  • Y (heterogeneous) - T:

    Output tensor

Type Constraints#

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

    Constrain input and output types to float tensors.