Versioned name: PReLU-1

Category: Activation

Short description: Parametric rectified linear unit element-wise activation function.

Detailed description: PReLU operation is introduced in this ‘article <>’. PReLU performs element-wise parametric ReLU operation on a given input tensor, based on the following mathematical formula:

\[\begin{split}PReLU(x) = \left\{\begin{array}{r} x \quad \mbox{if } x \geq 0 \\ \alpha x \quad \mbox{if } x < 0 \end{array}\right.\end{split}\]


  • data_format

    • Description: data_format denotes the data format of the input and output data.

    • Range of values: NXC or NCX (X means HW for 2D, DHW for 3D)

    • Type: string

    • Default value: NXC

    • Required: no

  • per_channel_broadcast

    • Description: per_channel_broadcast denotes whether to apply per_channel broadcast when slope is 1D tensor.

    • Type: boolean

    • Default value: True

    • Required: no


  • 1: data – input tensor. Required.

    • Type: T

  • 2: slope – slope tensor. Required.

    • Type: T


  • 1: The result tensor.

    • Type: T


  • T: f32, f16, bf16

  • Note: Inputs and outputs have the same data type denoted by T. For example, if input is f32 tensor, then all other tensors have f32 data type.

Broadcast rules:

Only slope tensor supports broadcast-semantics. Slope tensor is unidirectional broadcastable to data if one of the following rules is true:

  • 1: slope is 1D tensor and per_channel_broadcast = True, length of slope is equal to the length of data in channle dimensions.

  • 2: slope is 1D tensor and per_channel_broadcast = False, length of slope is equal to the length of data in the right most dimensions.

  • 3: slope is nD tensor, starting from the rightmost dimension, the two inputs tensor dimension sizes must be equal or slope size in that dimension 1, or slope in that dimension doesn’t exist.