Architecture of a CNN
The main components of a 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. 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:
Feature detector or filter
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...
