globalMaxPooling1dLayer
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
"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
| globalAveragePooling1dLayer
| exportNetworkToSimulink
| Global 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