GlobalAveragePooling1DLayer - 1-D global average pooling layer - MATLAB (original) (raw)

1-D global average pooling layer

Since R2021b

Description

A 1-D global average pooling layer performs downsampling by outputting the average of the time or spatial dimensions of the input.

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

• For time series and vector sequence input (data with three dimensions corresponding to the "C" (channel), "B" (batch), and "T" (time) dimensions), the layer pools over the"T" (time) dimension.
• For 1-D image input (data with three dimensions corresponding to the"S" (spatial), "C" (channel), and"B" (batch) dimensions), the layer pools over the"S" (spatial) dimension.
• For 1-D image sequence input (data with four dimensions corresponding to the"S" (spatial), "C" (channel),"B" (batch), and "T" (time) dimensions), the layer pools over the "S" (spatial) dimension.

Creation

Syntax

Description

`layer` = globalAveragePooling1dLayer creates a 1-D global average pooling layer.

example

`layer` = globalAveragePooling1dLayer(Name=name) sets the optional Name property.

Properties

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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 a 1-D global average pooling layer.

layer = globalMaxPooling1dLayer

layer = GlobalMaxPooling1DLayer with properties:

Name: ''

Define the neural network architecture.

layers = [ sequenceInputLayer(12,MinLength=20) convolution1dLayer(11,96) reluLayer globalAveragePooling1dLayer fullyConnectedLayer(10) softmaxLayer]

layers = 6×1 Layer array with layers:

 1   ''   Sequence Input               Sequence input with 12 dimensions
 2   ''   1-D Convolution              96 11 convolutions with stride 1 and padding [0  0]
 3   ''   ReLU                         ReLU
 4   ''   1-D Global Average Pooling   1-D global average pooling
 5   ''   Fully Connected              10 fully connected layer
 6   ''   Softmax                      softmax

Algorithms

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A 1-D global average pooling layer performs downsampling by outputting the average of the time or spatial dimensions of the input.

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

• For time series and vector sequence input (data with three dimensions corresponding to the "C" (channel), "B" (batch), and "T" (time) dimensions), the layer pools over the"T" (time) dimension.
• For 1-D image input (data with three dimensions corresponding to the"S" (spatial), "C" (channel), and"B" (batch) dimensions), the layer pools over the"S" (spatial) dimension.
• For 1-D image sequence input (data with four dimensions corresponding to the"S" (spatial), "C" (channel),"B" (batch), and "T" (time) dimensions), the layer pools over the "S" (spatial) dimension.

Global pooling layers remove the "T" (time) dimension when the pool over the "T" (time) dimension. Global pooling layers do not remove the "S" (spatial) dimension when the pool over the "S" (spatial) dimension.

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 GlobalAveragePooling1DLayer 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.

In dlnetwork objects, GlobalAveragePooling1DLayer objects also support these input and output format combinations.

GlobalAveragePooling1DLayer objects support complex-valued input and outputs. (since R2024a) The layer applies the same underlying operation to complex-valued input as it does to real-valued input and outputs complex-valued data where applicable.

Extended Capabilities

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Usage notes and limitations:

• You can generate generic C/C++ code that does not depend on third-party libraries and deploy the generated code to hardware platforms.

Usage notes and limitations:

• You can generate CUDA code that is independent of deep learning libraries and deploy the generated code to platforms that use NVIDIA® GPU processors.

Version History

Introduced in R2021b

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GlobalAveragePooling1DLayer objects support complex-valued input and outputs. The layer applies the same underlying operation to complex-valued input as it does to real-valued input and outputs complex-valued data where applicable.