averagePooling1dLayer
Description
A 1-D average pooling layer performs downsampling by dividing the input into 1-D pooling regions, then computing the average 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
creates a 1-D average pooling layer and sets thelayer
= averagePooling1dLayer(poolSize
)PoolSize
property.
还指定了填充或设置layer
= averagePooling1dLayer(poolSize
,Name=Value
)Stride
andNameproperties using one or more optional name-value arguments. For example,averagePooling1dLayer(3,Padding=1,Stride=2)
creates a 1-D average pooling layer with a pool size of3
, a stride of2
, and padding of size1
on both the left and right of the input.
Input Arguments
Properties
Examples
Algorithms
Version History
Introduced in R2021b
See Also
trainingOptions
|trainNetwork
|sequenceInputLayer
|lstmLayer
|bilstmLayer
|gruLayer
|convolution1dLayer
|maxPooling1dLayer
|globalMaxPooling1dLayer
|globalAveragePooling1dLayer
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