Applying pooling layers
A popular optimization technique for CNNs is pooling layers. A layer is a method to reduce the number of trainable parameters in a smart way. Two of the most commonly used pooling layers are average pooling and maximum (max) pooling. In the first, for a specified block size the inputs are averaged and extracted. For the latter, the maximum value within a block is extracted. These pooling layers provide a translational invariance. In other words, the exact location of a feature is less relevant. Also, by reducing the number of trainable parameters we limit the complexity of the network, which should prevent overfitting. Another benefit is that it will reduce the training and inference time significantly.
In the next recipe, we will add max pooling layers to the CNN we've implemented in the previous recipe and at the same time we will increase the number of filters in the convolutional layers.
How to do it...
- Import all necessary libraries:
import numpy as np from keras...
