Deep Learning (DL) advances have cleared the way for intriguing new applications and are influencing the future of Artificial Intelligence (AI) technology. However, a typical concern for DL models is their explainability, as experts commonly agree that Neural Networks (NNs) function as black boxes. We do not precisely know what happens inside, but we know that the given input is somehow processed, and as a result, we obtain something as output. For this reason, DL models can often be difficult to understand or interpret. Understanding why a model makes certain predictions or how to improve it can be challenging.
This article will introduce and emphasize the importance of NN explainability, provide insights into how to achieve it, and suggest tools that could improve your DL model’s performance.
The importance of explainability
The explainability of NN models is essential for several reasons. First, in the scientific domain, it is crucial to have as much control as possible over these “black boxes.”
The ability to quickly identify issues with the model helps developers save time and resources. If developers catch problems early in the process, they can avoid training models with fundamental issues.
In addition, it is crucial to investigate the presence or impact of underrepresented samples or categories in the dataset that can affect the model. Indeed, if a biased dataset is used for training a DL model, the model may learn and reinforce the biases present in the data, leading to inaccurate or discriminatory outcomes. This can have serious consequences, particularly in critical fields such as healthcare, finance, and justice, where decisions based on biased or inaccurate algorithms can have life-altering impacts.
As discussed above, datasets play a critical role in DL models and applications. They help the NNs understand how to react to various input stimuli, giving information on both input and the expected outcome.
When dealing with datasets, it is important to understand how the samples contribute to the DL model. This investigation is sometimes harder than it seems for several reasons.
Firstly, when handling labeled data, we must be careful that the labels are correct. In the case of ambiguous data, it can be difficult to determine the correct label. Furthermore, since labeling is a time-consuming and labor-intensive task, it is error-prone and can lead to mislabelling. Particularly with high-dimensional data, it can be challenging to label all features for all samples in a timely and accurate manner.
The problems and challenges mentioned above should motivate us to clean and fix the dataset after training the model. You might be wondering, “How can I do that?” Well, usually, this procedure requires manually looking into the dataset’s samples to check their validity, acknowledging factors leading to poor performance of the model, and re-evaluating the model.
Given the importance of dataset validation and the limitations of current approaches that are extremely time-consuming and error-prone, we began exploring alternative methods to see if there were quick and accurate alternatives.
Explainability Techniques and Tools
For model explainability, many strategies are employed. Some concentrate on global explainability, while others concentrate on local explainability.
Global explainability provides a comprehensive picture of the model’s interpretability. It assimilates how it creates predictions and how the network’s different properties impact the final prediction.
On the other hand, local explainability examines a single sample without offering a comprehensive knowledge of the model and is helpful for model debugging. In certain circumstances, using local explainability, the reasons for particular prediction mistakes can be found based on a single sample.
There are a few available tools that provide model explainability. The first one is DeepLIFT (Deep Learning Important FeaTures), which enables the explainability of a neural network’s predictions. However, it can be computationally expensive, as it requires multiple forward and backward passes through the NN to compute the reference activations used to compute the importance of each input feature for a particular prediction. Furthermore, it provides only global explainability. The second one is called Alibi, an open-source python library aimed at Machine Learning (ML) model inspection and interpretation. It supports the analysis of multiple data types, such as tabular, text, and image. However, Alibi mainly focuses on classical ML algorithms and provides only local explainability.
While most applications fail to provide sufficiently robust insights on global and local explainability, one new all-in-one tool, Tensorleap, can provide you with the complete package. It is a new must-have DL platform for every data science or ML expert. With this out-of-the-box platform, you can unleash the full potential of your neural networks, as it is the best way to troubleshoot, debug and visualize NNs and datasets. You can gain insights into your model’s local and global features with just a few clicks. The advanced management tool visualizes all findings, making it easy to improve datasets, test procedures, and the model. Furthermore, this platform can help you understand how the model perceives the data since it parses the computational graph, tracks every sample, and highlights the most informative features.
No other tool on the market gives you the same advanced features Tensorleap offers, such as Population Visualization, Deep Unit Testing, and Guided Error Analysis.
Dataset Visualization and Clean-up
It would be wrong to assert that the success of a NN model lies all in its architecture. A massive role is played by the dataset exploited for training, which means that the model will not see the sunlight if the dataset is faulty. The three most important steps to assess the bounty of a dataset are scoring, labeling prioritization, and dataset clean-up. Scoring refers to the dataset’s quality, measured in terms of variance, density, entropy, and balance. Labeling prioritization helps assess which data are most important to gather and how to label them correctly. Cleaning up the dataset from all redundant, ambiguous, and inadequate data in most cases improves the NN’s performance.
Tensorleap simplifies the detection and removal of erroneous data with Population Analysis. The platform tracks the response of each feature in each layer to each instance in the dataset, creating a similarity map based on the model’s interpretation of similar samples. This tool clusters and visually represents the samples, allowing you to identify which ones are causing problems in the dataset. Each dot is associated with a sample, and its size represents the impact of the error on the overall error of the NN. The influence of each dot on the overall error can be smartly analyzed using any custom metric or loss. With Tensorleap, you can quickly target and fix problems in your dataset or gain valuable insights for better results.
Deep Unit Testing
Software developers generally agree that unit testing is an essential phase in the software development process because it simplifies finding bugs and weak spots in the code. Unit testing the network’s components is equally crucial in DL models not only to find network logic errors, but also to continuously improve the performance of our model. Indeed, unit tests provide a safety net when applying changes to the model. Therefore, when you make changes to refine your model on specific categories, you can rerun the tests to ensure that the changes have not broken any existing functionality or impacted any other category. For instance, the model can be very good at detecting specific classes but very bad at detecting others. With this knowledge, the attention can be shifted where the network needs it most.
Deep Unit Testing in Tensorleap allows developers to test their DL models and inspect unwanted behavior. The tool enables the creation of multiple unit tests based on the model’s features or some properties of the samples, which can then be all validated simultaneously. Using a visual representation dashboard, you can focus on specific sample groups to identify and monitor specific unit tests. In the same way, Tensorleap allows you to conduct unsupervised analysis by utilizing the features of a model to identify potential clusters and abnormalities, providing an understanding of how the model performs in particular scenarios.
This process helps data scientists identify where improvements need to be made. Tensorleap automatizes and makes this process easier since manual testing is sometimes tedious or even impossible.
Error Analysis and Troubleshooting
Error analysis is important in DL as it helps reduce the cost of training a model. One hour of training a large model can cost hundreds or thousands of dollars, and if errors are found after training, it results in wasted money. Tensorleap provides an effective way to identify and fix errors in a model, saving time and money and avoiding potential risks in deployment. It visualizes network errors and automatically detects failures, allowing users to track issues and errors and improve model performance with precision. In addition, the platform provides insight into the model’s success or failures and reports when these failures occur more frequently. This solution eliminates the need for random testing and allows users to see where the model excels or needs improvement.
DL explainability is a vital element that allows our DL models to be developed faster and more robustly. Tensorleap gives data scientists the tools to construct reliable models and balanced datasets and enhance results while lowering development expenses. The platform is an excellent resource for data scientists who want to understand their NNs, lessen their associated failure risks, and improve their performance towards real issues that these models encounter.
If you want to try it, Tensorleap can be found following this link, along with well-written documentation.
Note: Thanks to the Tensorleap team for the thought leadership/ Educational article above. Tensorleap has supported and sponsored this Content.
Daniele Lorenzi received his M.Sc. in ICT for Internet and Multimedia Engineering in 2021 from the University of Padua, Italy. He is a Ph.D. candidate at the Institute of Information Technology (ITEC) at the Alpen-Adria-Universität (AAU) Klagenfurt. He is currently working in the Christian Doppler Laboratory ATHENA and his research interests include adaptive video streaming, immersive media, machine learning, and QoS/QoE evaluation.