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rpnSoftmaxLayer

Softmax layer for region proposal network (RPN)

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

Description

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

example

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

Properties

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Layer name, specified as a character vector or a string scalar. For Layer array input, the trainnet (Deep Learning Toolbox), trainNetwork (Deep Learning Toolbox), assembleNetwork (Deep Learning Toolbox), layerGraph (Deep Learning Toolbox), and dlnetwork (Deep Learning Toolbox) functions automatically assign names to layers with the name "".

The RPNSoftmaxLayer object stores this property as a character vector.

Data Types: char | string

This property is read-only.

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

Data Types: double

This property is read-only.

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

Data Types: cell

This property is read-only.

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

Data Types: double

This property is read-only.

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

Data Types: cell

Examples

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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 = 
  3x1 Layer array with layers:

     1   'conv1x1_box_cls'   2-D Convolution             6 1x1 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