globalMaxPooling1dLayer

1-D global max pooling layer

Description

A 1-D global max pooling layer performs downsampling by outputting the maximum 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 channels, observations, and time steps), the layer pools over the time dimension.

• For 1-D image input (data with three dimensions corresponding to the spatial pixels, channels, and observations), the layer pools over the spatial dimension.

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

Creation

Syntax

layer = globalMaxPooling1dLayer

layer = globalMaxPooling1dLayer(Name=name)

Description

layer = globalMaxPooling1dLayer creates a 1-D global max pooling layer.

layer = globalMaxPooling1dLayer(Name=name) sets the optional Name property.

Properties

Name — Layer name

'' (default) | character vector | string scalar

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

Data Types: char | string

NumInputs — Number of inputs

1 (default)

This property is read-only.

Number of inputs of the layer. This layer accepts a single input only.

Data Types: double

InputNames — Input names

{'in'} (default)

This property is read-only.

Input names of the layer. This layer accepts a single input only.

Data Types: cell

NumOutputs — Number of outputs

1 (default)

This property is read-only.

Number of outputs of the layer. This layer has a single output only.

Data Types: double

OutputNames — Output names

{'out'} (default)

This property is read-only.

Output names of the layer. This layer has a single output only.

Data Types: cell

Examples

Create 1-D Global Max Pooling Layer

Create a 1-D global max pooling layer.

layer = globalMaxPooling1dLayer

layer = 
  GlobalMaxPooling1DLayer with properties:

    Name: ''

Include a 1-D global max pooling layer in a layer array.

layers = [
    sequenceInputLayer(12)
    convolution1dLayer(11,96)
    reluLayer
    globalMaxPooling1dLayer
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer]

layers = 
  7x1 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 Max Pooling   1-D global max pooling
     5   ''   Fully Connected          10 fully connected layer
     6   ''   Softmax                  softmax
     7   ''   Classification Output    crossentropyex

Algorithms

1-D Global Max Pooling Layer

A 1-D global max pooling layer performs downsampling by outputting the maximum 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 channels, observations, and time steps), the layer pools over the time dimension.

• For 1-D image input (data with three dimensions corresponding to the spatial pixels, channels, and observations), the layer pools over the spatial dimension.

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

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 consists of one or more of these characters:

• "S" — Spatial

• "C" — Channel

• "B" — Batch

• "T" — Time

• "U" — Unspecified

For example, 2-D image data 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).

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

This table shows the supported input formats of GlobalMaxPooling1DLayer objects and the corresponding output format. If the output of the layer is passed to a custom layer that does not inherit from the nnet.layer.Formattable class, or a FunctionLayer object with the Formattable property set to 0 (false), then the layer receives an unformatted dlarray object with dimensions ordered corresponding to the formats in this table.

Input Format	Output Format
"SCB" (spatial, channel, batch)	"SCB" (spatial, channel, batch)
"CBT" (channel, batch, time)	"CB" (channel, batch)
"SCBT" (spatial, channel, batch, time)	"SCBT" (spatial, channel, batch, time)

Version History

Introduced in R2021b