Building Your First Convolution Neural Network — AlexNet

Using Keras Layers API

Now that you have read all my last articles on CNNs you are ready to build your first one using Keras. In this article, I will teach you two things: (1) how to read and interpret CNN architectures, and (2) how to build a CNN model using Keras basic building blocks.

In this article, I will use AlexNet as an example, as it is a reliable and simple CNN, but contains all the layers I have been writing about in the last articles.

Why AlexNet?

AlexNet is a CNN architecture that gained attention after winning the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) 2012. Developed by Alex Knizhevsky, Ilya Sutskever, and Geoffrey Hinton, AlexNet introduced the following concepts and design choices that become fundamental in subsequent CNN designs:

(1) Deep Architecture: AlexNet was one of the first CNNs to utilize a deep architecture with multiple layers. Its eight-layer design, including five convolutional layers and three fully connected layers, demonstrated the effectiveness of deep learning for image classification tasks.

(2) Convolutional layers with small receptive fields: AlexNet employed convolutional layers with small receptive fields, such as 3x3 and 5x5 to capture…