AdaptiveAveragePooling2DLayer - Adaptive average pooling 2-D layer - MATLAB (original) (raw)

Adaptive average pooling 2-D layer

Since R2024a

Description

A 2-D adaptive average pooling layer performs downsampling to give you the desired output size by dividing the input into rectangular pooling regions, then computing the average of each region.

Creation

Syntax

Description

`layer` = adaptiveAveragePooling2dLayer(`outputSize`) creates an adaptive average pooling layer and sets the OutputSize property.

`layer` = adaptiveAveragePooling2dLayer(Name=`name`) sets the optional Name property. For example, adaptiveAveragePooling2dLayer(16,Name="adap") creates an adaptive average pooling layer with output size [16 16] and sets the optional Name property.

example

Properties

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Adaptive Average Pooling

Dimensions of the output size, specified as a vector of two positive integers[sz1 sz2]. When creating the layer, you can specifyOutputSize as a scalar to use the same value for both dimensions.

Example: [12 14] specifies output size of 12 and 14 for the lengths of first and second dimensions respectively.

Layer

Data Types: char | string

This property is read-only.

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

Data Types: double

This property is read-only.

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

Data Types: cell

This property is read-only.

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

Data Types: double

This property is read-only.

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

Data Types: cell

Examples

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Create an adaptive average pooling layer with the output size [12 12] name "adap_avg1".

layer = adaptiveAveragePooling2dLayer(12,Name="adap_avg1")

layer = AdaptiveAveragePooling2DLayer with properties:

      Name: 'adap_avg1'
OutputSize: [12 12]

Learnable Parameters No properties.

State Parameters No properties.

Show all properties

Include an adaptive average pooling layer with the output size [12 12] in a Layer array. The layer automatically selects the stride and kernel-size to give you the specified output size.

layers = [ ... imageInputLayer([28 28 1]) convolution2dLayer(5,20) reluLayer adaptiveAveragePooling2dLayer(12) fullyConnectedLayer(10) softmaxLayer]

layers = 6×1 Layer array with layers:

 1   ''   Image Input                    28×28×1 images with 'zerocenter' normalization
 2   ''   2-D Convolution                20 5×5 convolutions with stride [1  1] and padding [0  0  0  0]
 3   ''   ReLU                           ReLU
 4   ''   2-D Adaptive Average Pooling   Adaptive average pooling with output size 12x12
 5   ''   Fully Connected                10 fully connected layer
 6   ''   Softmax                        softmax

Algorithms

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A 2-D adaptive average pooling layer performs downsampling by dividing the input into rectangular pooling regions, then computing the average of each region.

The dimensions that the layer pools over depends on the layer input:

• For 2-D image input (data with four dimensions corresponding to pixels in two spatial dimensions, the channels, and the observations), the layer pools over the spatial dimensions.
• For 2-D image sequence input (data with five dimensions corresponding to the pixels in two spatial dimensions, the channels, the observations, and the time steps), the layer pools over the spatial dimensions.
• For 1-D image sequence input (data with four dimensions corresponding to the pixels in one spatial dimension, the channels, the observations, and the time steps), the layer pools over the spatial and time dimensions.

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 format consists of one or more of these characters:

• "S" — Spatial
• "C" — Channel
• "B" — Batch
• "T" — Time
• "U" — Unspecified

For example, you can describe 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, as having the format "SSCB" (spatial, spatial, channel, batch).

You can interact with these dlarray objects in automatic differentiation workflows, such as those for developing a custom layer, using a functionLayer object, or using the forward and predict functions withdlnetwork objects.

This table shows the supported input formats of AdaptiveAveragePooling2DLayer objects and the corresponding output format. If the software passes the output of the layer to a custom layer that does not inherit from the nnet.layer.Formattable class, or aFunctionLayer object with the Formattable property set to 0 (false), then the layer receives an unformatted dlarray object with dimensions ordered according to the formats in this table. The formats listed here are only a subset. The layer may support additional formats such as formats with additional "S" (spatial) or"U" (unspecified) dimensions.

References

[1] Nagi, J., F. Ducatelle, G. A. Di Caro, D. Ciresan, U. Meier, A. Giusti, F. Nagi, J. Schmidhuber, L. M. Gambardella. ''Max-Pooling Convolutional Neural Networks for Vision-based Hand Gesture Recognition''.IEEE International Conference on Signal and Image Processing Applications (ICSIPA2011), 2011.

Extended Capabilities

Version History

Introduced in R2024a