The Architecture of a CNN

The main components of CNN architecture are as follows:

• Input image
• Convolutional layer
• Pooling layer
• Flattening

Input Image

An input image forms the first component of a CNN architecture. An image can be of any type: a human, an animal, scenery, a medical X-ray image, and so on. Each image is converted into a mathematical matrix of zeros and ones. The following figure explains how a computer views an image of the letter T.

All the blocks that have a value of one represent the data, while the zeros represent blank space:

Figure 7.3: Matrix for the letter 'T'

Convolution Layer

The convolution layer is the place where image processing starts. A convolution layer consists of two parts:

• Feature detector or filter
• Feature map

Feature detector or a filter: This is a matrix or pattern that you put on an image to transform it into a feature map:

Figure...