R Deep Learning Essentials - Second Edition

Pooling layers

Pooling layers are used in CNNs to reduce the number of parameters in the model and therefore they reduce overfitting. They can be thought of as a type of dimensionality reduction. Similar to convolutional layers, a pooling layer moves over the previous layer but the operation and return value are different. It returns a single value and the operation is usually the maximum value of the cells in that patch, hence the name max-pooling. You can also perform other operations, for example, average pooling, but this is less common. Here is an example of max-pooling using a 2 x 2 block. The first block has the values 7, 0, 6, 6 and the maximum value of these is 7, so the output is 7. Note that padding is not normally used with max-pooling ...