The One-Stop Guide to Convolutional Neural Networks

The purpose of convolutional layers, as mentioned previously are to extract features or details from an image. A complete CNN will have many convolutional layers. As a general trend, deeper layers will extract specific shapes for example eyes from an image, while shallower layers extract more general shapes like lines and curves. Pooling layers are used to reduce the size of the convolved map. This decreases the number of parameters needed to train and the computing power needed to train the model.