Introduction to Convolutional Neural Networks: Code Example with Three Pooling Layers in CNN

Convolutional Neural Networks (CNN) are classical deep learning models for processing image data, where pooling operations serve as one of the essential components. Pooling layers perform downsampling to reduce data dimensionality while preserving critical features.

Implementing a CNN model with three pooling layers in MATLAB involves these key steps: First, define the network architecture, typically including convolutional layers, activation functions, and pooling layers. The pooling operation can be implemented using either max pooling or average pooling. Second, apply three consecutive pooling layers to progressively reduce feature map dimensions - each pooling operation typically halves the spatial dimensions.

For beginners, this example demonstrates the fundamental feature extraction pipeline in CNNs: as network depth increases, pooling operations make feature maps smaller but more abstract. In practical implementation, after three pooling operations with 2x2 pooling windows and stride 2, the feature map size becomes 1/8 of the original input dimensions.

After understanding this basic example, learners can further explore different pooling strategies (like global pooling) or combine other layers (such as batch normalization layers) to optimize network performance. The code typically uses MATLAB's maxPooling2dLayer() or averagePooling2dLayer() functions with specified pool size and stride parameters to implement each pooling stage.