Architecture of a CNN

The main components of a CNN architecture are as follows:

1. Input image

2. Convolutional layer

3. Pooling layer

4. 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. At a very high level, 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 represents blank space:

Figure 7.3: Matrix for the letter 'T'

Convolution Layer

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

1. Feature detector or filter

2. Feature map

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

Figure 7.4: Feature detector

Now, as highlighted, this feature detector is put (superimposed) on the original image and...