Pooling layers reduce the dimensionality of feature maps produced by convolutional layers. They summarize information within a region or across the entire sequence, helping improve efficiency and generalization.
| Layer | Description | Parameters |
|---|---|---|
| MaxPooling1D | Retains the maximum value within each window, reducing the size of feature maps while preserving their number. | Pool Size — Size of the pooling window. |
Once you have configured the model settings and architecture, you are ready to start training. See the Model Results section to learn how to initiate training and evaluate model performance.