globalMaxPooling1dLayer
1-D global max pooling layer
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 channels, observations, and time steps), the layer pools over the time dimension.
For 1-D image input (data with three dimensions corresponding to the spatial pixels, channels, and observations), the layer pools over the spatial dimension.
For 1-D image sequence input (data with four dimensions corresponding to the spatial pixels, channels, observations, and time steps), the layer pools over the spatial dimension.
Creation
Properties
Examples
Algorithms
Version History
Introduced in R2021b
See Also
trainingOptions
| trainNetwork
| sequenceInputLayer
| lstmLayer
| bilstmLayer
| gruLayer
| convolution1dLayer
| maxPooling1dLayer
| averagePooling1dLayer
| 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 Networks
- List of Deep Learning Layers
- Deep Learning Tips and Tricks