maxPooling1dLayer

1-D max pooling layer

Since R2021b

Description

A 1-D max pooling layer performs downsampling by dividing the input into 1-D pooling regions, then computing the maximum of each region.

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

layer = maxPooling1dLayer(poolSize)

layer = maxPooling1dLayer(poolSize,Name=Value)

Description

layer = maxPooling1dLayer(poolSize) creates a 1-D max pooling layer and sets the PoolSize property.

layer = maxPooling1dLayer(poolSize,Name=Value) sets optional properties using one or more name-value arguments.

Input Arguments

poolSize — Width of pooling regions

positive integer

Width of the pooling regions, specified as a positive integer.

The width of the pooling regions poolSize must be greater than or equal to the padding dimensions PaddingSize.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: maxPooling1dLayer(3,Stride=2) creates a 1-D max pooling layer with a pool size of 3 and stride 2.

Stride — Step size for traversing input

1 (default) | positive integer

Step size for traversing the input, specified as a positive integer.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Padding — Padding to apply to input

[0 0] (default) | "same" | nonnegative integer | vector of nonnegative integers

Padding to apply to the input, specified as one of the following:

• "same" — Apply padding such that the output size is ceil(inputSize/stride), where inputSize is the length of the input. When Stride is 1, the output is the same size as the input.

• Nonnegative integer sz — Add padding of size sz to both ends of the input.

• Vector [l r] of nonnegative integers — Add padding of size l to the left and r to the right of the input.

Example: Padding=[2 1] adds padding of size 2 to the left and size 1 to the right.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | char | string

Name — Layer name

"" (default) | character vector | string scalar

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

This argument sets the Name property.

Data Types: char | string

Properties

Max Pooling

PoolSize — Width of pooling regions

positive integer

Width of the pooling regions, specified as a positive integer.

The width of the pooling regions PoolSize must be greater than or equal to the padding dimensions PaddingSize.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Stride — Step size for traversing input

1 (default) | positive integer

Step size for traversing the input, specified as a positive integer.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

PaddingSize — Size of padding

[0 0] (default) | vector of two nonnegative integers

Size of padding to apply to each side of the input, specified as a vector [l r] of two nonnegative integers, where l is the padding applied to the left and r is the padding applied to the right.

When you create a layer, use the Padding name-value argument to specify the padding size.

Data Types: double

PaddingMode — Method to determine padding size

Read-only: 'manual' (default) | 'same'

This property is read-only.

Method to determine padding size, specified as one of the following:

• 'manual' – Pad using the integer or vector specified by Padding.

• 'same' – Apply padding such that the output size is ceil(inputSize/Stride), where inputSize is the length of the input. When Stride is 1, the output is the same as the input.

To specify the layer padding, use the Padding name-value argument.

Data Types: char

Layer

Name — Layer name

'' (default) | character vector | string scalar

Layer name, specified as a character vector or a string scalar. For Layer array input, the trainnet and dlnetwork functions automatically assign names to unnamed layers.

The MaxPooling1DLayer object stores this property as a character vector.

Data Types: char | string

NumInputs — Number of inputs

Read-only: 1 (default)

This property is read-only.

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

Data Types: double

InputNames — Input names

Read-only: {'in'} (default)

This property is read-only.

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

Data Types: cell

NumOutputs — Number of outputs

Read-only: 1 (default)

This property is read-only.

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

Data Types: double

OutputNames — Output names

Read-only: {'out'} (default)

This property is read-only.

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

Data Types: cell

Examples

Create 1-D Max Pooling Layer

Create a 1-D max pooling layer with a pool size of 3.

layer = maxPooling1dLayer(3);

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

layers = [
    sequenceInputLayer(12,MinLength=40)
    convolution1dLayer(11,96)
    reluLayer
    maxPooling1dLayer(3)
    convolution1dLayer(11,96)
    reluLayer
    globalMaxPooling1dLayer
    fullyConnectedLayer(10)
    softmaxLayer]

layers = 
  9×1 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 Max Pooling          Max pooling with pool size 3, stride 1, and padding [0  0]
     5   ''   1-D Convolution          96 11 convolutions with stride 1 and padding [0  0]
     6   ''   ReLU                     ReLU
     7   ''   1-D Global Max Pooling   1-D global max pooling
     8   ''   Fully Connected          10 fully connected layer
     9   ''   Softmax                  softmax

Algorithms

1-D Max Pooling Layer

A 1-D max pooling layer performs downsampling by dividing the input into 1-D pooling regions, then computing the maximum of each region. The layer pools the input by moving the pooling regions along the input horizontally.

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.

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 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 with dlnetwork objects.

This table shows the supported input formats of MaxPooling1DLayer 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 a FunctionLayer 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.

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)
"SB" (spatial, batch)	"SB" (spatial, batch)

In dlnetwork objects, MaxPooling1DLayer objects also support these input and output format combinations.

Input Format	Output Format
"SC" (spatial, channel)	"SC" (spatial, channel)
"CT" (channel, time)	"CT" (channel, time)
"SCT" (spatial, channel, time)	"SCT" (spatial, channel, time)
"BT" (batch, time)	"BT" (batch, time)
"SBT" (spatial, batch, time)	"SBT" (spatial, batch, time)

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

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.

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

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