Whether they’re analyzing data, debugging their models, or simply using AI-powered applications, downstream users of neural networks will benefit from visualization. For example, Facebook’s ActiVis is a visual analytics system tailored to engineers and data scientists.
Visualizations can help explain how a model arrives at specific decisions by displaying its underlying feature representations.
Understanding Neural Networks
Neural network visualization is an algorithm modeled loosely after the human brain to recognize patterns. They perform a machine perception, labeling or clustering raw input into categories, like images, sounds, text, and time series. They can be used to do such things as identifying objects, faces, and voices in self-driving cars and voice assistants, interpreting natural language for sentiment analysis and chatbots; diagnosing diseases from medical images; and making predictions or determinations on future outcomes such as sales or stock market movements.
A neural network model is a pipeline of interconnected neurons or nodes with a series of hidden layers that use complex algorithms to learn about the input. The solution is then put in an output layer as a prediction or determination.
One common way to train a neural network is through supervised learning. This is where the network is given a set of examples, and the desired output from those examples is compared with the actual outcomes of the web to see how close it can get. The network can then revise its free parameters to match the desired result more closely.
A visualization tool included in the tools package, NeuralNetTools, can help visualize neural networks by plotting them. It also contains functions to evaluate a neural network using a neural interpretation diagram (NID), conduct a variable importance analysis (Garson or Olden algorithm), and run a sensitivity analysis (lek-profile). The NID is useful for visualizing model architecture, while the other three are helpful in quantifying model complexity.
Visualizing Neural Networks
Visualization methods can help ML engineers understand how models work at training and inference times. Whether a model underperforms or generates suspiciously good results, it is often difficult to pinpoint the root cause without visualization. For this reason, visualization tools are a valuable addition to the toolkits of data scientists and machine learning engineers.
They are visualizing activation heatmaps, revealing which parts of the input the model considers important, which can be particularly useful in computer vision and NLP. Similarly, feature visualization shows the abstract features the model recognizes (e.g., diagonal edges in images or grammatical structures in text). It helps us see how the model interprets the input.
While existing plot functions, such as the popular neural net package in R, can be used to visualize a neural network, their utility needs to expand. For example, while the sensitivity analysis method in the neural net package helps assess the impact of changes to a model’s weighted connections, it can be impractical to evaluate many variables using this technique alone.
Other visualization techniques aim to provide more granular data that can be used to debug and explain neural networks. For instance, gradient-based saliency methods, like the Grad-CAM and LIME algorithms, can identify regions of the input emphasized by different layers in a neural network. However, these methods are model-aware and only work for information with a certain shape, such as 2D arrays of scalar values or RGB triples.
Identifying Neural Networks
Neural networks are a popular way to implement machine learning, a form of artificial intelligence that makes computers learn from and act like human beings. They can perform classification, clustering, or prediction tasks by identifying patterns in data or images corresponding to particular class outcomes.
A neural network will typically learn to perform these tasks by training in advance on examples that have been hand-labeled. For example, an object recognition system might be fed thousands of labeled images of cars, houses, and coffee cups to identify the visual patterns that distinguish these objects. As the training progresses, the network will adjust many of its weights to find a balance that translates signal to meaning as correctly as possible. A network’s error and its consequences are closely related, and a small change in the error will yield a slight change in the weight.
Visualizing these weights helps us understand how a neural network might classify an image. For example, the weights that assign a high value to an edge in a scene might be shown as orange, and those that attach a low importance to an area might be blue. It will highlight the regions the network will most likely focus on when attempting to predict. It can help you discover why it might misclassify an image or how to improve its accuracy.
Identifying Patterns in Neural Networks
Pattern recognition is finding similarities in data using machine learning algorithms. Neural networks can find patterns where humans can’t see them, like when it comes to identifying objects in images or understanding the contents of a video clip without being told what the content is about.
For example, neural networks can recognize faces in a photo. They can also detect and classify speech, analyze text to recognize words and phrases or read medical images to identify tumors or diseases.
One way to improve a neural network’s ability to recognize patterns is to train it with a large dataset of images labeled with the correct classification. Then, the web will have more training examples to learn from when making predictions on unseen data.
Another technique for enhancing a neural network’s interpretability is mapping or approximating the complex model’s predictions to a more understandable space. Different visualization methods are available for this purpose, ranging from gradient-based practices that backpropagate the network output towards its input to perturbation-based techniques such as t-SNE.
Conclusion
A simple way to visualize a neural network’s activations is by plotting the intensity of each neuron over time. For example, this visualization of the first hidden layer of a convolutional neural network shows how neurons gradually learn to recognize more complex features from lower-level ones, such as edges or colors.