Utility Function In Artificial Intelligence – By mixing simple object-oriented programming concepts such as functionalization and class inheritance, you can add tremendous value to deep learning prototyping code.
This post is not intended for experienced software engineers. This is intended for data scientists and machine learning (ML) practitioners who, like me, do not come from a software engineering background.
Utility Function In Artificial Intelligence
We use Python a lot for our work. Why? Because it’s great for the ML and data science community.
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It is on track to become the fastest growing major language for modern analytics and data-driven artificial intelligence (AI) applications.
However, it is also used for simple scripting purposes, to automate things, test hypotheses, create interactive brainstorming plots, control lab instruments, etc.
Python for software development and Python for scripting are not exactly the same beast – at least in the field of data science.
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Scripting is (mostly) code that you write yourself. Software is a set of code that you (and other teammates) write for others.
It would be wise to admit that when (most) non-software engineering data scientists write Python programs for AI/Ml modeling and statistical analysis, they tend to write such code.
They just want to get to the heart of the pattern hidden in the data. Fast. Without thinking deeply about mere mortals –
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They write blocks of code to produce a rich and beautiful plot. But they don’t create a function from it for later use.
They import many methods and classes from standard libraries. But they don’t create their own subclass through inheritance and don’t add methods to extend functionality.
Functions, inheritance, methods, classes – it’s the heart of robust object-oriented programming (OOP), but it’s somewhat avoidable if all you want to do is create a Jupyter notebook with your data analysis and diagrams.
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You can avoid the initial pain of using OOP principles, but this almost always makes your notebook code unusable and extensible.
In short, that part of the code serves only you (until you forget what logic you coded) and no one else.
But readability (and thus reusability) is critically important. It is a true test of the value of what you have produced. Not for myself. But for others.
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On top of that, hundreds of popular MOOCs or online courses on data science and AI/ML also don’t emphasize this aspect of coding, as it seems like a burden for a young, enthusiastic learner. He/she is here to learn cool neural network algorithms and optimizations, not OOP in Python. Therefore, this aspect remains neglected.
You can avoid the initial pain of using OOP principles, but it makes your notebook code unusable and unextensible. A simple mix of OOP can sharpen your deep learning (DL) code
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I’m not a software engineer, never have been in my life. So when I started researching ML and data science, I wrote massive amounts of messy, non-reusable code.
Little by little I’m trying to get better and use simple improvements to my coding style to make it more useful (for everyone in the world).
And I’ve found that it doesn’t take much to mix OOP principles into your data code.
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Even if you’ve never taken a software engineering course in your life, some ideas may come naturally to you. All you have to do is put yourself in someone else’s shoes and think about how that person will accept and use your code in a constructive way.
What do I even mean by all this? Let us demonstrate on a simple case – the problem of classification of a DL image with a fashion MNIST data set.
And I’ve found that it doesn’t take much to mix OOP principles into your data code. Illustration of a case with the DL classification task: an approach
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A detailed notebook is available here in my Github repo. You are encouraged to go through it and fork it for your own use and expansion.
Code is essential to building great software, but it’s not necessarily appropriate for a middling article that you read to gain insight rather than practice a debugging or refactoring exercise.
So I’ll just jot down selected code snippets and try to find out how I tried to code some of the principles previously detailed in this notebook.
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The fundamental task of ML is simple – to build a deep learning classifier for the fashionable MNIST dataset, which is a fun twist on the original famous MNIST dataset of handwritten digits. Fashion MNIST consists of 60,000 28 x 28 pixel training images – fashion-related objects, eg hats, shoes, pants, t-shirts, clothes, etc. It also consists of 10,000 test images for model validation and testing.
But what if there is a question of higher order optimization or visual analysis surrounding this fundamental ML task – how does the complexity of the model architecture affect the minimum epochs required to achieve the desired accuracy?
It should be clear to the reader why we even bother with such a question. Because it is related to the overall optimization of the business. Training a neural network is not a trivial computational task. Therefore, it makes sense to investigate what minimum training effort must be invested to achieve a target performance metric and how this is affected by the choice of architecture.
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We won’t even use a convolutional network in this example, because a simple densely connected neural network can achieve reasonably high accuracy, and in fact somewhat suboptimal performance is needed to illustrate the main point of the higher-order optimization question we posed above.
Here we show some code snippets to illustrate how simple OOP principles are used to achieve our solution. Clips are marked with comments for easy understanding.
First, we inherit from the Keras class and write our own subclass for a method to check the training accuracy and perform an action based on that value.
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This simple recall results in dynamic epoch control – training stops automatically when the accuracy reaches the desired threshold.
We put the Keras model construction codes in a helper function so that a model of an arbitrary number of layers and architecture (as long as they are tightly coupled) can be generated with simple user input in the form of a few function arguments.
We can even put the compilation and training code in a helper function to conveniently use these hyperparameters in a higher-order optimization loop.
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Next, it’s time to visualize. And here we go through the practice of functionalization. Generic plotting functions take raw data as input. However, if we have the specific purpose of plotting the evolution of the accuracy of the training set and showing the comparison to the target, our plotting function should simply take a deep learning model as input and generate the desired graph.
And now we can take all the functions and classes we defined earlier and combine them all to achieve a higher order task.
Therefore, our final code will be super compact, but will generate the same interesting plots of loss and accuracy over epochs that we show above, for different accuracy threshold values and neural network architectures.
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This gives the user the ability to use a minimal amount of code to create a visual analysis on the choice of performance metric (in this case accuracy) and neural network architecture. This is the first step in building an optimized machine learning system.
Our final analysis/optimization code is concise and easy to follow for the advanced user who does not need to know the complexities of Keras model building or callback classes.
This is the core principle behind OOP – the abstraction of layers of complexity that we can achieve for our deep learning task.
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To an instance of the class. Although we needed the basic status push for initial checking/debugging, we should run the analysis silently for the optimization task. If we didn’t have this argument in our class definition, we would have no way to stop the debug messages.
We show some of the representative results that are automatically generated by executing the code block above. It clearly shows how with a minimal amount of high-level code we can generate a visual analysis to evaluate the relative performance of different neural architectures for different levels of performance metrics. This allows the user, without changing the functions of the lower levels, to easily make a judgment about the choice of the model according to its performance requirements.
Also note the custom titles for each lot. These headings clearly explain the target performance and complexity of the neural network, making analytics simple.
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It was a small addition to the utility plotting function, but it shows the need for careful planning when creating such functions. If we hadn’t planned such an argument for the function, it wouldn’t have been possible to generate a custom title for each diagram. This careful planning of the API (application program interface) is an integral part of good OOP.
Until now, you
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