preluLayer

Parametrized Rectified Linear Unit (PReLU) layer

Since R2024a

Description

A PReLU layer performs a threshold operation, where for each channel, any input value less than zero is multiplied by a scalar learned at training time.

This operation is equivalent to:

$$f(x_i)=\{\begin{array}{cc}{x}_i& \text{if }{x}_i>0\\ {\alpha }_i{x}_i& \text{if }{x}_i\le 0\end{array}$$

Creation

Syntax

layer = preluLayer

layer = preluLayer(Name=Value)

Description

layer = preluLayer returns a parametrized ReLU layer.

layer = preluLayer(Name=Value) returns a parametrized ReLU layer and sets the optional Name and Alpha properties. For example, preluLayer(Alpha=2,Name="prelu1") creates a PReLU layer with the optional Alpha and Name properties.

Properties

PReLU

Alpha — Learnable multiplier for negative input values

0.25 (default) | numeric scalar or vector or matrix | numeric vector | numeric matrix

Learnable multiplier for negative input values, specified either as a numeric scalar, or vector, or a matrix. The size of Alpha must be compatible with the input size of the PReLU layer. If the sizes of Alpha and the input of the PReLU layer are compatible, then the two arrays implicitly expand to match each other. For example, if Alpha is a scalar, then the scalar is combined with each element of the other array. Also, vectors with different orientations (one row vector and one column vector) implicitly expand to form a matrix.

The network learns the parameter Alpha during training.

Example: 0.4

Layer

Name — Layer name

"" (default) | character vector | string scalar

Layer name, specified as a character vector or a string scalar. For Layer array input, the trainnet and dlnetwork functions automatically assign names to layers with the name "".

The PReLULayer object stores this property as a character vector.

Data Types: char | string

NumInputs — Number of inputs

1 (default)

This property is read-only.

Number of inputs to the layer, returned as 1. This layer accepts a single input only.

Data Types: double

InputNames — Input names

{'in'} (default)

This property is read-only.

Input names, returned as {'in'}. This layer accepts a single input only.

Data Types: cell

NumOutputs — Number of outputs

1 (default)

This property is read-only.

Number of outputs from the layer, returned as 1. This layer has a single output only.

Data Types: double

OutputNames — Output names

{'out'} (default)

This property is read-only.

Output names, returned as {'out'}. This layer has a single output only.

Data Types: cell

Examples

Create PReLU Layer

Create a PReLU layer with the name "prelu1".

layer = preluLayer(Name="prelu1")

layer = 
  PReLULayer with properties:

     Name: 'prelu1'

   Learnable Parameters
    Alpha: 0.2500

   State Parameters
    No properties.

Use properties method to see a list of all properties.

Include a PReLU layer in a Layer array.

layers = [
    imageInputLayer([28 28 1])
    convolution2dLayer(3,16)
    batchNormalizationLayer
    preluLayer
 
    maxPooling2dLayer(2,Stride=2)
    convolution2dLayer(3,32)
    batchNormalizationLayer
    preluLayer
    
    fullyConnectedLayer(10)
    softmaxLayer]

layers = 
  10x1 Layer array with layers:

     1   ''   Image Input           28x28x1 images with 'zerocenter' normalization
     2   ''   2-D Convolution       16 3x3 convolutions with stride [1  1] and padding [0  0  0  0]
     3   ''   Batch Normalization   Batch normalization
     4   ''   PReLU                 PReLU
     5   ''   2-D Max Pooling       2x2 max pooling with stride [2  2] and padding [0  0  0  0]
     6   ''   2-D Convolution       32 3x3 convolutions with stride [1  1] and padding [0  0  0  0]
     7   ''   Batch Normalization   Batch normalization
     8   ''   PReLU                 PReLU
     9   ''   Fully Connected       10 fully connected layer
    10   ''   Softmax               softmax

Algorithms

Layer Input and Output Formats

Layers in a layer array or layer graph pass data to subsequent layers as formatted dlarray objects. The format of a dlarray object is a string of characters, in which each character describes the corresponding dimension of the data. The formats consist of one or more of these characters:

• "S" — Spatial

• "C" — Channel

• "B" — Batch

• "T" — Time

• "U" — Unspecified

For example, 2-D image data that is represented as a 4-D array, where the first two dimensions correspond to the spatial dimensions of the images, the third dimension corresponds to the channels of the images, and the fourth dimension corresponds to the batch dimension, can be described as having the format "SSCB" (spatial, spatial, channel, batch).

PReLULayer objects apply an element-wise operation and supports input data of any format. The layer and does not add or remove any dimensions, so it outputs data with the same format as its inputs.

References

[1] Maas, Andrew L., Awni Y. Hannun, and Andrew Y. Ng. "Rectifier nonlinearities improve neural network acoustic models." In Proc. ICML, vol. 30, no. 1. 2013.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Version History

Introduced in R2024a