# maxPooling1dLayer

## 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

### Description

sets optional properties using one or more name-value arguments.`layer`

= maxPooling1dLayer(`poolSize`

,`Name=Value`

)

### Input Arguments

## Properties

## Examples

## Algorithms

## Extended Capabilities

## Version History

**Introduced in R2021b**

## See Also

`trainnet`

| `trainingOptions`

| `dlnetwork`

| `sequenceInputLayer`

| `lstmLayer`

| `bilstmLayer`

| `gruLayer`

| `convolution1dLayer`

| `averagePooling1dLayer`

| `globalMaxPooling1dLayer`

| `globalAveragePooling1dLayer`

| `exportNetworkToSimulink`

| Max Pooling 1D
Layer

### Topics

- Sequence Classification Using 1-D Convolutions
- Sequence-to-Sequence Classification Using 1-D Convolutions
- Sequence Classification Using Deep Learning
- Sequence-to-Sequence Classification Using Deep Learning
- Sequence-to-Sequence Regression Using Deep Learning
- Time Series Forecasting Using Deep Learning
- Long Short-Term Memory Neural Networks
- List of Deep Learning Layers
- Deep Learning Tips and Tricks