rpnSoftmaxLayer

R2026b

(Not recommended) Softmax layer for region proposal network (RPN)

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RPNSoftmaxLayer is not recommended. Instead, use a different type of object detector, such as a yolov4ObjectDetector detector. For more information, see Version History.

Description

A region proposal network (RPN) softmax layer applies a softmax activation function to the input. Use this layer to create a Faster R-CNN object detection network.

Creation

Syntax

layer = rpnSoftmaxLayer
layer = rpnSoftmaxLayer('Name',Name)

Description

layer = rpnSoftmaxLayer creates a softmax layer for a Faster R-CNN object detection network.

layer = rpnSoftmaxLayer('Name',Name) creates a softmax layer and sets the optional Name property.

example

Properties

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Layer name, specified as a character vector. For Layer array input, the trainnet (Deep Learning Toolbox) and dlnetwork (Deep Learning Toolbox) functions automatically assign names to unnamed layers.

Data Types: char

This property is read-only.

Number of inputs to the layer, represented as 1. This layer has a single input only.

Data Types: double

This property is read-only.

Input name, represented as {'in'}. This layer has a single input only.

This property is read-only.

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

Data Types: double

This property is read-only.

Output name, represented as {'out'}. This layer has a single output only.

Examples

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Open Live Script

Create an RPN softmax layer with the name 'rpn_softmax'.

rpnSoftmax = rpnSoftmaxLayer('Name','rpn_softmax')
rpnSoftmax = 
  RPNSoftmaxLayer with properties:

    Name: 'rpn_softmax'

Create an RPN classification layer with the name 'rpn_cls'.

rpnClassification = rpnClassificationLayer('Name','rpn_cls')
rpnClassification = 
  RPNClassificationLayer with properties:

    Name: 'rpn_cls'

Add the RPN softmax and RPN classification layers to a Layer array, to form the classification branch of an RPN.

numAnchors = 3;
rpnClassLayers = [
    convolution2dLayer(1,numAnchors*2,'Name','conv1x1_box_cls')
    rpnSoftmax
    rpnClassification
    ]
rpnClassLayers = 
  3×1 Layer array with layers:

     1   'conv1x1_box_cls'   2-D Convolution             6 1×1 convolutions with stride [1  1] and padding [0  0  0  0]
     2   'rpn_softmax'       RPN Softmax                 rpn softmax
     3   'rpn_cls'           RPN Classification Output   cross-entropy loss with 'object' and 'background' classes

Version History

Introduced in R2018b

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See Also

(Deep Learning Toolbox)

Topics