Exploring Neural Network Graphs: Understanding the Visual Representation of Deep Learning Models

Learning how to visualize your neural network can not only help you understand the underlying data flow of your model, but can also help you optimize, debug, and validate your neural network. By learning a few basic neural networks, common applications for this type of model, and how to read a basic neural network graph, you can build the foundation needed to start exploring this type of visualization on your own.

Understanding neural network graphs

Neural network graphs are representations of the underlying structure and connectivity of neural networks. When you look at a neural network graph, you can see a visual map of how layers and nodes are organized within your model. This depicts the flow of information through the network, demonstrating how data passes from the input layer through hidden layers and finally to the output layer. The neurons of the graph are typically represented by nodes, and the lines between nodes represent the weighted connections between these neurons. 

Your neural network graph will vary depending on the type of model you use. Common ones you might choose include:

• Feedforward neural networks (FNN): FNNs are the simplest type of neural network, illustrating a one-way, forward flow of information through layers. You might use this type of algorithm for image classification, speech recognition, or even natural language processing.

• Recurrent neural networks (RNN): RNNs include loops to previous nodes, which allows your model to retain previous information and make predictions about future inputs. This is commonly used for sequential or time-series data, such as language modeling.

• Convolutional neural networks (CNN): CNNs have specialized layers, called convolution layers and pooling layers, that allow them to work with three-dimensional data. This is important for image, sound, and audio analysis.

Components of neural network graphs

To represent a neural network, you need to incorporate several types of visual representations for the model’s structure. While the hierarchy will vary depending on your exact model, some components typically included in your diagram will be:

Nodes:

Nodes represent neurons, also known as processing units, within each layer. Each node receives an input, processes it through an activation function, and passes it to the appropriate node within the following layer.

Edges:

Edges represent the connection between nodes, drawn as a line or arrow between them. This represents the way your data flows between the neurons. If your data has several pathways it can take, you might have arrows from certain nodes going in multiple directions. 

Layers: 

Each set of nodes represents a layer. For example, if you have layers A → B → C → D, layer A would be your input layer, layers B and C would be your hidden layers, and layer D would be your output layer. Depending on the network type, you may have a different volume of hidden layers between the input and output nodes. 

Activation function:

In your graphical representation, you might show activation functions, which are equations that take the input value and convert it to a new value that is passed along to the next node. Depending on this new value, the neuron might make a decision about which pathway to send the information down. 

Weights and biases: 

While not included in every graph, weights and biases influence the output of each neuron. Including these in your graph can provide a more accurate visualization of the information pathway and decision-making process. 

Practical applications of neural network graphs

As a professional, you might use neural network graphs to represent your model for a variety of applications. By mapping out each layer, these graphs allow you to visualize the flow of information and refine how information transforms through your model. Some fields where you might see more widespread use of artificial neural networks include the following:

Speech and language recognition

In speech and language recognition systems, neural network graphs help you visualize complex architectures, like recurrent neural networks (RNNs), which are popular for speech processing, language translation functions, and voice conversion and synthesis. For example, smart devices often use this type of technology to allow you to converse with voice assistants or auto-dictate text. 

Image and video processing

In visual spaces, convolutional neural networks (CNNs) are common choices for computer vision tasks such as object detection and image segmentation. This helps professionals in fields such as health care classify images, such as identifying abnormalities in medical images for more accurate diagnoses. It’s also important for security features, such as facial recognition systems, allowing devices to recognize unique individuals and determine whether to grant access. 

Autonomous driving and sensors

Autonomous driving systems typically have multi-sensor architectures, meaning they combine inputs from cameras, radar, and more. This allows self-driving vehicles to perceive their surroundings—such as road signs, pedestrians, and obstacles—with CNNs, and use this information to make decisions that ensure safe transportation. Having an accurate neural network graph is important to ensure the algorithm functions effectively and safely, helping professionals gain a comprehensive understanding of the underlying decision-making system. 

Personalized recommendation systems

Recommendation systems use neural network graphs to analyze user behavior, allowing for specialized suggestions based on their content preferences. This type of system often has to combine several types of data, such as product images and text descriptions. As the system obtains more information from users, it can update predictions and refine recommendations. This involves complex architectures with many hidden layers, and industry leaders often adopt different approaches to how best to develop their models. Drawing a neural network graph in these cases can help you compare options, consider how information would flow through each architecture, and make the right decision for your data.

Tools to start visualizing neural network graphs

To start visualizing your neural network graph and exploring different options, software tools can help you better understand complex models and identify areas for improvement. A few software tools that can help you explore different diagrams and create informative visualizations include:

• Python: Libraries like Matplotlib can help you create custom visualizations, including building neural network diagrams.

• GraphViz: An open-source software, GraphViz allows you to create neural networks, including labels on inputs and outputs for each node. 

• TensorBoard: TensorBoard software allows you to map out your flow, including node expansions for more detailed information.

• Elegant neural network user interface (ENNUI): ENNUI includes a drag-and-drop interface to help you visualize the neural network learning process.

• NN-SVG: This is a neural network schematic designed to create publication-ready neural network architecture diagrams.

Learn more about deep learning on Coursera

Neural network graphs are a powerful visualization tool that can help you understand the intricacies of deep learning algorithms. Continue exploring neural networks and deep learning on Coursera with courses and Specializations taught by industry leaders. For an intermediate-level course, try Neural Networks and Deep Learning by DeepLearning.AI, which is one of the five courses included in the Deep Learning Specialization available on Coursera.