Implementation of Pooling Layers Using Python Programming

Pooling layers

In the CNN architecture, pooling layers come after the convolution layer and perform pooling operations on the feature maps (outputs from the convolution layer) in order to downsample the input data. Their primary objective is to decrease the spatial dimensionality of the input, which results in a reduced representation. This reduction offers several benefits, including a decrease in the number of parameters, as well as reduced learning time and computational complexity within the network.

Pooling layers also help prevent overfitting by downsampling the input, which reduces the risk of memorizing specific training data and encourages generalization. They also make the subsequent layers more computationally efficient by reducing the spatial dimensions.

Here are some common pooling operations performed in the pooling layer.

Max pooling

Max pooling is a pooling operation that selects the maximum value within a region of the feature map covered by a pool size in the pooling layer. The resulting output retains the most prominent features from the previous feature map by applying max pooling. This process helps to capture and highlight the most significant features, aiding in feature extraction and dimensionality reduction in the network. Max pooling is a commonly used pooling technique in DL. The image below demonstrates the process of max pooling with a pool size of 2x2.