Neural networks have become increasingly popular in recent years, with applications ranging from image classification to natural language processing (NLP). With every new application, there is an ever-increasing need for better architectures of neural networks!

A common starting point when designing a neural network is choosing what kind of layer you will use to process your data. There are many different types of layers that can be connected together to form your final model, so deciding which one is best depends mostly on your problem domain.

That’s why it is important to understand the three fundamental components of each layer type before picking your final structure: how they work, where they come from, and what their advantages and disadvantages are. In this article, we will discuss two such layer types: convolutional layers and fully connected layers.

We will also look at some basic configurations of these layers and how to visualize the architecture of our models using graphs and matrices. This way, you will get more intuition about how individual parts of a neural network work.

To make the concepts easier to apply, we will focus only on small examples: building a binary classifier to decide whether or not someone has enough money to live on.

Once everything is set up, we will compare and evaluate several different architectures to see which one works the best.

Look at examples of neural networks

There are many ways to visualize different architectures of neural networks. You can use formulas for functions as inputs, you can input layers such as fully connected or convolutional, and how the network is connected overall.

There are also tools that allow you to create your own architecture. This includes creating structures such as feedforward nets or recurrent networks like LSTMs or GRUs.

And while some require programming knowledge, most do not. Some even have easy interfaces where you just pick and choose what components you want to include and connect them together.

Here we will go over several methods to understand neural net architectures. What matters is not only which one is best, but whether you are more familiar with software engineering or coding.

If you’re already comfortable working in those areas then why not add deep learning to your toolbox? If you’re not quite there yet then this article will be helpful for developing your skills.

Try to visualize what each layer does

The first step in designing neural network architectures is trying to understand how individual layers work. Each layer of the network has its own function, which can be complicated or very straightforward depending on what it is.

Some examples of simple layers are max-pooling layers that take an input array and create new arrays by taking subsets of surrounding information and finding the maximum value in every subset, or dropout layers that randomly remove units (elements) from the input so you cannot use that unit as a predictor for the rest of the model!

Other more complex layers do things like convolution where they look at small overlapping windows of adjacent parts of the image and find patterns within those groups of pixels. These pattern recognition networks then combine all these features into one vector representation of the whole picture to determine if something is recognizable as part of the category “cat” or not.

The number of neurons in a given layer affects the size of the window being looked at and the type of neuron used will affect how much weight is assigned to different features in the layer above it. Neurons with larger activation values mean that the feature was important in determining the output of the layer above it.

If we keep adding more and more layers onto our architecture, the overall system becomes increasingly difficult to evaluate because there are too many variables. This is why having some general rules of thumb is helpful when starting out.

Connect each layer in order

Layer size is an important factor in determining how well a neural network functions. The larger your layers, the higher the chance of overfitting, which means that the model will learn overly specific patterns in the dataset.

This happens when the model predicts every example in the training set correctly with high accuracy, but can’t apply this knowledge to new examples it has never seen before.

In other words, even though the model had enough time to learn all the features of the data, it doesn’t work because it only learned about the ones it was given during training!

If you think about it, our brain works by using neurons to recognize patterns. As we know, humans are really good at recognizing things quickly so they have built lots of neuron connections into their brains through repeated exposure to experiences.

That’s why it takes us just a few seconds to realize what type of person someone is after watching them for several years. But creating those connections requires repeated exposures which is where practice comes in!

So if you want your neural networks to perform better, make sure your layers are connected properly and contain large units or “layers”. Make the layers as small as possible while still producing quality results, otherwise you may be paying too much attention to detail and ignoring the bigger picture.

Link each layer to its output

The second step in designing neural network architectures is linking each layer to its corresponding target. This process is called link or connection activation.

Typically, we have input layers that receive data from our dataset, one or more hidden layers with nonlinear functions applied, and then an output layer that calculates the result that you want your model to predict.

The number of links between each layer and the next depends on the size of the datasets you are working with and the results that you want to achieve. If your model does not improve when linked, you may choose to leave out those connections to save CPU time. You can always add them later!

Linked or unlinked, every layer contributes to the final prediction by applying some sort of mathematical function to their neighboring layers’ outputs. These functions vary depending on what kind of task the layer will be used for, but most use linear terms as well.

For example, if your model predicts whether someone has access to a credit card or not, your classification layer would calculate a value indicating whether they do or don’t, and then sum up all these values with other similar classes to determine the overall score.

That way, even people who barely meet the criteria for having a credit card could get flagged due to how many other people in the class had one.

Link each layer to its input

The second most important component of any neural network architecture is linking each layer to its corresponding input. This link comes from how every layer computes its output by looking at its input set and applying an operation to it.

For example, say we have a very simple neural net with one hidden layer and two outputs. One output will be for predicting whether or not there are more than 5 people in a picture, and the other will be for determining if something is natural or man-made.

The inputs would be a photo of anything (no matter what), a classification of “natural”, and a classification of “man made”. The hidden layer would apply some math operation to combine these three inputs into one single number.

This number would depend on which area of the photo was paying attention to detail, how much both types of details were, and then taking this overall value and deciding if the image was more focused on either type of detail or not.

Try to identify important layers in a neural network

As mentioned before, understanding which layer of a neural net is performing well can help you determine what kind of results you are getting and how to improve them. One way to do this is to try to identify whether each layer is doing its job- learning and altering patterns depending on the input it receives.

By looking at the layers sequentially, you can also tell when a given layer is not helping much with your training process. If a layer does not seem to be changing much after many iterations, then remove it! Reversing this process will probably help you figure out which parts of the neural network work best and should be left in place.

There are several strategies for removing layers from a neural network. You may want to simply test out different configurations or find ones that perform worse than others.

Use transfer learning

Recent developments in deep learning have focused on creating networks that perform specific tasks. These so-called task-specific networks use pretrained models as starting points to learn how to accomplish a given task.

A common example of this is using an image recognition model like Google’s Inception V3 network to teach your neural net how to recognize cats. You would start with an uninformed neural net and then slowly add layers and training data for the cat class until it got pretty good at it.

This concept can be extended into other areas such as natural language processing (NLP). A commonly used architecture for NLP are recurrent neural networks (RNN) or long short term memory (LSTM) networks, which work by taking input sequences and combining it with previous inputs and internal state vectors to form new information.

By applying this approach to different domains, we get domain adaptation. Domain adaptation means teaching one network architecture to do something for one set of data, and then adapting it to another set without completely redoing the work. This is particularly useful when there is no labeled dataset available for you to begin with!

Extending beyond individual architectures

Another emerging field in AI is what’s called multi-task learning or multitask optimization. Rather than having separate networks for each task, all the weights are shared between them. The individual networks are optimized separately, but also for their ability to improve performance on the others.

Modify the neural network structure

The second way to modify the architecture of a neural network is by altering the number of layers or the type of layer you have in your model.

There are two types of layers that most architectures contain – input and output layers, and one or more transition (or dense) layers in between.

The transition layer goes through an activation function before producing its own set of results. This depends on what kind of result you want your net to predict.

For example, if your goal is to classify images as either cat pictures or dog pictures, then having an additional transition layer which chooses whether to give zero, one, or both labels depending on how well it identifies features of each class would be appropriate.

This is called a softmax regression network because it creates probability estimates for every possible outcome instead of just picking one. Probability alone isn’t very useful though- it needs to be applied to some sort of utility function to determine the best choice, so that part of the process stays the same!

How to use this article

To begin, we will look at how to create a simple convolutional neural network using Keras. Then, we will discuss ways to add new layers to your network.