This article provides a easy to understand introduction to what convolutional neural networks are and how they work.
What are Convolutional Neural Networks and why are they important?
Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self driving cars.
In Figure 1 above, a ConvNet is able to recognize scenes and the system is able to suggest relevant tags such as ‘bridge’, ‘railway’ and ‘tennis’ while Figure 2 shows an example of ConvNets being used for recognizing everyday objects, humans and animals. Lately, ConvNets have been effective in several Natural Language Processing tasks (such as sentence classification) as well.
ConvNets, therefore, are an important tool for most machine learning practitioners today. However, understanding ConvNets and learning to use them for the first time can sometimes be an intimidating experience. The primary purpose of this blog post is to develop an understanding of how Convolutional Neural Networks work on images.
If you are new to neural networks in general, I would recommend reading this short tutorial on Multi Layer Perceptrons to get an idea about how they work, before proceeding. Multi Layer Perceptrons are referred to as “Fully Connected Layers” in this post.
LeNet was one of the very first convolutional neural networks which helped propel the field of Deep Learning. This pioneering work by Yann LeCun was named LeNet5 after many previous successful iterations since the year 1988 [3]. At that time the LeNet architecture was used mainly for character recognition tasks such as reading zip codes, digits, etc.
Below, we will develop an intuition of how the LeNet architecture learns to recognize images. There have been several new architectures proposed in the recent years which are improvements over the LeNet, but they all use the main concepts from the LeNet and relatively easier to understand if you have a clear understanding of the former.
The Convolutional Neural Network in Figure 3 is similar in architecture to the original LeNet and classifies an input image into four categories: dog, cat, boat or bird (the original LeNet was used mainly for character recognition tasks). As evident from the figure above, on receiving a boat image as input, the network correctly assigns the highest probability for boat (0.94) among all four categories. The sum of all probabilities in the output layer should be one (explained later in this post).
There are four main operations in the ConvNet shown in Figure 3 above:
These operations are the basic building blocks of every Convolutional Neural Network, so understanding how these work is an important step to developing a sound understanding of ConvNets.
