# globalAveragePooling1dLayer

## 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

## Properties

## Examples

## Algorithms

## Extended Capabilities

## Version History

**Introduced in R2021b**

## See Also

`trainnet`

| `trainingOptions`

| `dlnetwork`

| `sequenceInputLayer`

| `lstmLayer`

| `bilstmLayer`

| `gruLayer`

| `convolution1dLayer`

| `maxPooling1dLayer`

| `averagePooling1dLayer`

| `globalMaxPooling1dLayer`

| `exportNetworkToSimulink`

| Global Average 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