How to Train a Convolutional Neural Network in Simple Terms

It’s hard to explain how convolutional neural networks are trained without getting into some advanced mathematical concepts. The closest thing to a simplified explanation without being too vague would be that training CNNs (or neural networks in general) is a process of trial and error. 


In a nutshell, neural networks rely on a series of mathematical “recipes” and ingredients. Abstracting very liberally, these networks cook the recipes and then see how well the food tastes. 


The results (cooked food in the analogy above) inform how the recipes will change to make future results better. 


The basics of convolutional neural networks (ConvNet) were first introduced in a 2015 paper, which brokered the idea that AI deep learning could understand how to merge the content of a source image with the style of a second. While that might not sound extraordinary, it's a huge milestone in countless applications, from medical imaging to facial recognition, and, of course, the potential of AI-generated artwork.


If you want to turn a picture into a painting, a trained ConvNet system can do just that!


And, if you’re interested in learning more details about neural networks, how their trained, and perhaps even creating your own, a good place to start is OpenAIs primer on training large neural networks

What Are Neural Networks?


A neural network mimics our thought processes in our brains, with nodes replicating the neurons that connect all of our functions. There are tons of hidden layers within that network, with multiple nodes on each. A weighted value is assigned to the input on each node, which dictates how the interpreted changes affect the result.


All sorts of neural networks are used in machine learning, such as feed-forward, modular, and recurrent networks, but we use convolutional networks in AI artwork creation. ConvNet uses filters that separate every aspect of an image, using the capacity of AI to learn what constitutes a subject and a style.


Given the complexity of neural networks, it takes time to fine-tune them to achieve perfect outcomes, but with in-depth testing and training, you get a lightning-fast AI tool that has almost limitless capacity. Next time you make a photo look like an oil painting, you'll have an idea of the intricacies of the AI behind the process that makes it all possible.

How Do Convolutional Neural Networks Work?


The secret sauce that makes ConvNet so remarkable is that it processes data by sorting it into a grid, extracting just those all-important features. Digital art creators can input any source image without pre-processing.


Like most image handling algorithms, ConvNet filters are created by engineers and enable the AI to learn about which characteristics within each image are meaningful–you don't need to enter parameters because the AI will work it out for itself.


One key benefit is processing huge image files and high-resolution images with thousands of pixels. ConvNet works to sort the data into a framework that is easily processed but doesn't lose any of the original properties.

What Does ‘Convolutional’ Mean?


The word ‘convolutional’ is far from as convoluted as it might sound! Instead, it's a quick way to refer to something difficult to follow, like a spiral, elaborate shape, or a highly complex AI system.


ConvNet handles all the maths behind your image creation, so it relies on convolutions (think of them as spins of the wheel) rather than matrix multiplications. Deep learning functionality helps ConvNet finesse its filters in real-time as it happens. 


Try downloading a video of your artwork transformation, and you'll see ConvNet working out what to do and reimagining your photo, drawing, or picture, piece by piece. At NightCafe, we train ConvNet by updating filters, which gives us greater flexibility to apply higher numbers of relevant filters, meaning you can select any artwork style you wish and watch the magic happen.