<p>“The human brain has 100 billion neurons, each neuron connected to 10 thousand other neurons. Sitting on your shoulders is the most complicated object in the known universe.”</p>
<p>— Michio Kaku</p>
</blockquote>
<p>I began with inanimate objects living in a world of forces, and gave them desires, autonomy, and the ability to take action according to a system of rules. Next, I allowed those objects, now called creatures, to live in a population and evolve over time. Now I’d like to ask: What is each creature’s decision-making process? How can it adjust its choices by learning over time? Can a computational entity process its environment and generate a decision?</p>
<p>The human brain can be described as a biological neural network—an interconnected web of neurons transmitting elaborate patterns of electrical signals. Dendrites receive input signals and, based on those inputs, fire an output signal via an axon. Or something like that. How the human brain actually works is an elaborate and complex mystery, one that I certainly am not going to attempt to tackle in rigorous detail in this chapter.</p>
<figure>
<imgsrc="images/10_nn/10_nn_1.png"alt="Figure 10. An illustration of a neuron with dendrites and an axon connected to another neuron.">
<figcaption>Figure 10. An illustration of a neuron with dendrites and an axon connected to another neuron.</figcaption>
</figure>
<p>The good news is that developing engaging animated systems with code does not require scientific rigor or accuracy, as you've learned throughout this book. You can simply be inspired by the idea of brain function.</p>
<p>In this chapter, I'll begin with a conceptual overview of the properties and features of neural networks and build the simplest possible example of one (a network that consists of a single neuron). I’ll then introduce you to more complex neural networks using the ml5.js library. Finally, I'll cover “neuroevolution”, a technique that combines genetic algorithms with neural networks to create a “Brain” object that can be inserted into the <code>Vehicle</code> class and used to calculate steering.</p>
<h2id="artificial-neural-networks-introduction-and-application">Artificial Neural Networks: Introduction and Application</h2>
<p>Computer scientists have long been inspired by the human brain. In 1943, Warren S. McCulloch, a neuroscientist, and Walter Pitts, a logician, developed the first conceptual model of an artificial neural network. In their paper, "A logical calculus of the ideas immanent in nervous activity,” they describe the concept of a neuron, a single cell living in a network of cells that receives inputs, processes those inputs, and generates an output.</p>
<p>Their work, and the work of many scientists and researchers that followed, was not meant to accurately describe how the biological brain works. Rather, an <em>artificial</em> neural network (hereafter referred to as a “neural network”) was designed as a computational model based on the brain to solve certain kinds of problems.</p>
<p>It’s probably pretty obvious to you that there are problems that are incredibly simple for a computer to solve, but difficult for you. Take the square root of 964,324, for example. A quick line of code produces the value 982, a number your computer computed in less than a millisecond. There are, on the other hand, problems that are incredibly simple for you or me to solve, but not so easy for a computer. Show any toddler a picture of a kitten or puppy and they’ll be able to tell you very quickly which one is which. Say “hello” and shake my hand one morning and you should be able to pick me out of a crowd of people the next day. But need a machine to perform one of these tasks? Scientists have already spent entire careers researching and implementing complex solutions.</p>
<p>The most prevalent use of neural networks in computing today involves these “easy-for-a-human, difficult-for-a-machine” tasks known as pattern recognition. These encompass a wide variety of problem areas, where the aim is to detect, interpret, and classify data. This includes everything from identifying objects in images, recognizing spoken words, understanding and generating human-like text, and even more complex tasks such as predicting your next favorite song or movie, teaching a machine to win at complex games, and detecting unusual cyber activities.</p>
<p>One of the key elements of a neural network is its ability to <em>learn</em>. A neural network is not just a complex system, but a complex <strong><em>adaptive</em></strong> system, meaning it can change its internal structure based on the information flowing through it. Typically, this is achieved through the adjusting of <em>weights</em>. In the diagram above, each line represents a connection between two neurons and indicates the pathway for the flow of information. Each connection has a <strong><em>weight</em></strong>, a number that controls the signal between the two neurons. If the network generates a “good” output (which I'll define later), there is no need to adjust the weights. However, if the network generates a “poor” output—an error, so to speak—then the system adapts, altering the weights in order to improve subsequent results.</p>
<p>There are several strategies for learning, and I'll examine two of them in this chapter.</p>
<ul>
<li><strong><em>Supervised Learning</em></strong> —Essentially, a strategy that involves a teacher that is smarter than the network itself. For example, let’s take the facial recognition example. The teacher shows the network a bunch of faces, and the teacher already knows the name associated with each face. The network makes its guesses, then the teacher provides the network with the answers. The network can then compare its answers to the known “correct” ones and make adjustments according to its errors. Our first neural network in the next section will follow this model.</li>
<li><strong><em>Unsupervised Learning</em></strong> —Required when there isn’t an example data set with known answers. Imagine searching for a hidden pattern in a data set. An application of this is clustering, i.e. dividing a set of elements into groups according to some unknown pattern. I won’t be showing at any examples of unsupervised learning in this chapter, as this strategy is less relevant for the examples in this book.</li>
<li><strong><em>Reinforcement Learning</em></strong> —A strategy built on observation. Think of a little mouse running through a maze. If it turns left, it gets a piece of cheese; if it turns right, it receives a little shock. (Don’t worry, this is just a pretend mouse.) Presumably, the mouse will learn over time to turn left. Its neural network makes a decision with an outcome (turn left or right) and observes its environment (yum or ouch). If the observation is negative, the network can adjust its weights in order to make a different decision the next time. Reinforcement learning is common in robotics. At time <code>t</code>, the robot performs a task and observes the results. Did it crash into a wall or fall off a table? Or is it unharmed? I'll showcase how reinforcement learning works in the context of our simulated steering vehicles.</li>
</ul>
<p>Reinforcement learning comes in many variants and styles. In this chapter, while I will lay the groundwork of neural networks using supervised learning, my primary focus will be a technique related to reinforcement learning known as <em>neuroevolution</em>. This method builds upon the code from chapter 9 and "evolves" the weights (and in some cases, the structure itself) of a neural network over generations of "trial and error" learning. It is especially effective in environments where the learning rules are not precisely defined or the task is complex with numerous potential solutions. And yes, it can indeed be applied to simulated steering vehicles!</p>
<p>A neural network itself is a “connectionist” computational system. The computational systems I have been writing in this book are procedural; a program starts at the first line of code, executes it, and goes on to the next, following instructions in a linear fashion. A true neural network does not follow a linear path. Rather, information is processed collectively, in parallel throughout a network of nodes (the nodes, in this case, being neurons).</p>
<p>Here I am showing yet another example of a complex system, much like the ones seen throughout this book. Remember how the individual boids in a flocking system, following only three rules—separation, alignment, cohesion, created complex behaviors? The individual elements of a neural network network are equally simple to understand. They read an input, a number, process it, and generate an output, another number. A network of many neurons, however, can exhibit incredibly rich and intelligent behaviors, echoing the complex dynamics seen in a flock of boids.</p>
<p>This ability of a neural network to learn, to make adjustments to its structure over time, is what makes it so useful in the field of artificial intelligence. Here are some standard uses of neural networks in software today.</p>
<ul>
<li><strong><em>Pattern Recognition</em></strong> — As I’ve discussed, this is one of the most common applications, with examples that range from facial recognition and optical character recognition to more complex tasks like gesture recognition.</li>
<li><strong><em>Time Series Prediction and Anomaly Detection</em></strong> — Neural networks are utilized both in forecasting, such as predicting stock market trends or weather patterns, and in recognizing anomalies, which can be applied to areas like cyberattack detection and fraud prevention.</li>
<li><strong><em>Natural Language Processing (or “NLP” for short)</em></strong> — One of the biggest developments in recent years has been the use of neural networks for processing and understanding human language. They are used in various tasks including machine translation, sentiment analysis, text summarization, and are the underlying technology behind many digital assistants and chat bots.</li>
<li><strong><em>Signal Processing and Soft Sensors</em></strong> — Neural networks play a crucial role in devices like cochlear implants and hearing aids by filtering noise and amplifying essential sounds. They're also involved in 'soft sensor' scenarios, where they process data from multiple sources to give a comprehensive analysis of the environment.</li>
<li><strong><em>Control and Adaptive Decision-Making Systems</em></strong> — These applications range from autonomous systems like self-driving cars and drones, to adaptive decision-making used in game playing, pricing models, and recommendation systems on media platforms.</li>
<li><strong><em>Generative Models</em></strong> — The rise of novel neural network architectures has made it possible to generate new content. They are used for synthesizing images, enhancing image resolution, style transfer between images, and even generating music and video.</li>
</ul>
<p>This is by no means a comprehensive list of applications of neural networks. But hopefully it gives you an overall sense of the features and possibilities. Today, leveraging machine learning in creative coding and interactive media is not only feasible, but increasingly common. Two libraries that you may want to consider exploring further for working with neural networks are tensorflow.js and ml5.js. TensorFlow.js<strong></strong>is an open-source library that lets you define, train, and run machine learning models in JavaScript. It's part of the TensorFlow ecosystem, which is maintained and developed by by Google. ml5.js is a library built on top of tensorflow.js designed specifically for use with p5.js. It’s goal is to be beginner friendly and make machine learning approachable for a braod audience of artists, creative coders, and students.</p>
<p>One of the more common things to do with tensorflow.js and ml5.js is to use something known as a “pre-trained model.” A “model” in machine learning is a specific setup of neurons and connections and a “pre-trained” model is one that has already been trained on a dataset for a particular task. It can be used “as is” or as a starting point for additional learning (commonly referred to as “transfer learning”).</p>
<p>Examples of popular pretrained models are ones that can classify images, identify body poses, recognize facial landmarks or hand positions, or even analyze the sentiment expressed in a text. Covering the full gamit of possibilities in this rapidly expanding and evolving space probably merits an entire additional book, maybe a series of books. And by the time that book was printed it would probably be out of date.</p>
<p>So instead, for me, as I embark on this last hurrah in the nature of code, I’ll stick to just two things. First, I’ll look at how to build the simplest of all neural networks from scratch using only p5.js. The goal is to gain an understanding of how the concepts of neural networks and machine learning are implemented in code. Second, I’ll explore one library, specifically ml5.js, which offers the ability to create more sophisticated neural network models and use them to drive simulated vehicles.</p>
<h2id="the-perceptron">The Perceptron</h2>
<p>Invented in 1957 by Frank Rosenblatt at the Cornell Aeronautical Laboratory, a perceptron is the simplest neural network possible: a computational model of a single neuron. A perceptron consists of one or more inputs, a processor, and a single output.</p>
<figure>
<imgsrc="images/10_nn/10_nn_3.png"alt="Figure 10.3: The perceptron">
<figcaption>Figure 10.3: The perceptron</figcaption>
</figure>
<p>A perceptron follows the “feed-forward” model, meaning inputs are sent into the neuron, are processed, and result in an output. In the diagram above, this means the network (one neuron) reads from left to right: inputs come in, output goes out.</p>
<p>Let’s follow each of these steps in more detail.</p>
<p>Each input sent into the neuron must first be weighted, meaning it is multiplied by some value, often a number between -1 and 1. When creating a perceptron, the inputs are typically assigned random weights. Let’s give the example inputs the following weights:</p>
<table>
<thead>
<tr>
<th>Weight</th>
<th>Value</th>
</tr>
</thead>
<tbody>
<tr>
<td>w0</td>
<td>0.5</td>
</tr>
<tr>
<td>w1</td>
<td>-1</td>
</tr>
</tbody>
</table>
<p>The next step is each input and multiply it by its weight.</p>
<p>The output of a perceptron is produced by passing the sum through an activation function. Think about a “binary” output, one that is only “off” or “on” like an LED. In this case, the activation function determines whether the perceptron should "fire" or not. If it fires, the light turns on; otherwise, it remains off.</p>
<p>Activation functions can get a little bit hairy. If you start reading about activation functions in artificial intelligence textbooks, you may find yourself reaching for a calculus textbook. However, with your new friend the simple perceptron, there’s an easy option which demonstrates the concept. Let’s make the activation function the sign of the sum. In other words, if the sum is a positive number, the output is 1; if it is negative, the output is -1.</p>
<p>Step #1 "for every input" implies a loop that multiplies each input by its corresponding weight. To obtain the sum, the results can be added up in that same loop.</p>
<preclass="codesplit"data-code-language="javascript">// Steps 1 and 2: Add up all the weighted inputs.
let sum = 0;
for (let i = 0; i < inputs.length; i++) {
sum += inputs[i] * weights[i];
}</pre>
<p>With the sum, I can then compute the output.</p>
<preclass="codesplit"data-code-language="javascript">// Step 3: Passing the sum through an activation function
let output = activate(sum);
// The activation function
function activate(sum) {
//{!5} Return a 1 if positive, -1 if negative.
if (sum > 0) {
return 1;
} else {
return -1;
}
}</pre>
<h2id="simple-pattern-recognition-using-a-perceptron">Simple Pattern Recognition Using a Perceptron</h2>
<p>Now that I have explained the computational process of a perceptron, let's take a look at an example of one in action. As I mentioned earlier, neural networks are commonly used for pattern recognition applications, such as facial recognition. Even simple perceptrons can demonstrate the fundamentals of classification. Let’s demonstrate with the following scenario.</p>
<p>Consider a line in two-dimensional space. Points in that space can be classified as living on either one side of the line or the other. While this is a somewhat silly example (since there is clearly no need for a neural network; on which side a point lies can be determined with some simple algebra), it shows how a perceptron can be trained to recognize points on one side versus another.</p>
<p>Let’s say a perceptron has 2 inputs: <spandata-type="equation">x,y</span> coordinates of a point). When using a sign activation function, the output will either be -1 or 1. The input data are classified according to the sign of the output, the weighted sum of inputs. In the above diagram, you can see how each point is either below the line (-1) or above (+1).</p>
<p>The perceptron itself can be diagrammed as follows. In machine learning <spandata-type="equation">x</span>’s are typically the notation for inputs and <spandata-type="equation">y</span> is typically the notation for an output. To keep this convention I’ll note in the diagram the inputs as <spandata-type="equation">x_0</span> and <spandata-type="equation">x_1</span>. <spandata-type="equation">x_0</span> will correspond to the x cooordinate and <spandata-type="equation">x_1</span> to the y. I name the output simply “<spandata-type="equation">\text{output}</span>”.</p>
<imgsrc="images/10_nn/10_nn_5.png"alt="Figure 10.5 Two inputs (x_0 and x_1), a weight for each input (\text{weight}_0 and \text{weight}_1) as well as a processing neuron that generates the output.">
<figcaption>Figure 10.5 Two inputs (<spandata-type="equation">x_0</span> and <spandata-type="equation">x_1</span>), a weight for each input (<spandata-type="equation">\text{weight}_0</span> and <spandata-type="equation">\text{weight}_1</span>) as well as a processing neuron that generates the output.</figcaption>
<p>There is a pretty significant problem in Figure 10.5, however. Let’s consider the point <spandata-type="equation">(0,0)</span>. What if I send this point into the perceptron as its input: <spandata-type="equation">x_0 = 0</span> and <spandata-type="equation">x_1=1</span>? What will the sum of its weighted inputs be? No matter what the weights are, the sum will always be 0! But this can’t be right—after all, the point <spandata-type="equation">(0,0)</span> could certainly be above or below various lines in this two-dimensional world.</p>
<p>To avoid this dilemma, the perceptron requires a third input, typically referred to as a <strong><em>bias</em></strong> input. A bias input always has the value of 1 and is also weighted. Here is the perceptron with the addition of the bias:</p>
<figure>
<imgsrc="images/10_nn/10_nn_6.png"alt="Figure 10.6: Diagram of a perceptron with the added “bias” input.">
<figcaption>Figure 10.6: Diagram of a perceptron with the added “bias” input.</figcaption>
</figure>
<p>Let’s go back to the point <spandata-type="equation">(0,0)</span>.</p>
<p>The output is then the sum of the above three results: <spandata-type="equation">0 + 0 + w_\text{bias}</span>. Therefore, the bias, by itself, answers the question of where <spandata-type="equation">(0,0)</span> is in relation to the line. If the bias's weight is positive, then <spandata-type="equation">(0,0)</span> is above the line; if negative, it is below. Its weight <strong><em>biases</em></strong> the perceptron's understanding of the line's position relative to <spandata-type="equation">(0,0)</span>!</p>
<h2id="coding-the-perceptron">Coding the Perceptron</h2>
<p>I am now ready to assemble the code for a <code>Perceptron</code> class. The perceptron only needs to track the input weights, which I can store using an array.</p>
<p>The constructor could receive an argument indicating the number of inputs (in this case three: <spandata-type="equation">x_0</span>, <spandata-type="equation">x_1</span>, and a bias) and size the array accordingly.</p>
<preclass="codesplit"data-code-language="javascript"> // The argument "n" determines the number of inputs (including the bias)
constructor(n) {
this.weights = [];
for (let i = 0; i < n; i++) {
//{!1} The weights are picked randomly to start.
this.weights[i] = random(-1, 1);
}
}</pre>
<p>A perceptron’s job is to receive inputs and produce an output. These requirements can be packaged together in a <code>feedForward()</code> function. In this example, the perceptron's inputs are an array (which should be the same length as the array of weights), and the output is an a number, <spandata-type="equation">+1</span> or <spandata-type="equation">-1</span>, depending on the sign as returned by the activation function.</p>
for (let i = 0; i < this.weights.length; i++) {
sum += inputs[i] * this.weights[i];
}
//{!1} Result is the sign of the sum, -1 or +1.
// Here the perceptron is making a guess.
// Is it on one side of the line or the other?
return this.activate(sum);
}</pre>
<p>I’ll note that the name of the function "feed forward" in this context comes from a commonly used term in neural networks to describe the process data passing through the network. This name relates to the way the data <em>feeds</em> directly <em>forward</em> through the network, read from left to right in a neural network diagram.</p>
<p>Presumably, I could now create a <code>Perceptron</code> object and ask it to make a guess for any given point.</p>
<preclass="codesplit"data-code-language="javascript">// Create the Perceptron.
let perceptron = new Perceptron(3);
// The input is 3 values: x, y, and bias.
let inputs = [50, -12, 1];
// The answer!
let guess = perceptron.feedForward(inputs);</pre>
<p>Did the perceptron get it right? At this point, the perceptron has no better than a 50/50 chance of arriving at the right answer. Remember, when I created it, I gave each weight a random value. A neural network is not a magic tool that can guess things correctly on its own. I need to teach it how to do so!</p>
<p>To train a neural network to answer correctly, I will use the method of <em>supervised learning</em>, which I described in section 10.1. In this method, the network is provided with inputs for which there is a known answer. This enables the network to determine if it has made a correct guess. If it is incorrect, the network can learn from its mistake and adjust its weights. The process is as follows:</p>
<ol>
<li>Provide the perceptron with inputs for which there is a known answer.</li>
<li>Ask the perceptron to guess an answer.</li>
<li>Compute the error. (Did it get the answer right or wrong?)</li>
<li>Adjust all the weights according to the error.</li>
<li>Return to Step 1 and repeat!</li>
</ol>
<p>Steps 1 through 4 can be packaged into a function. Before I can write the entire function, however, I need to examine Steps 3 and 4 in more detail. How do I define the perceptron’s error? And how should I adjust the weights according to this error?</p>
<p>The perceptron’s error can be defined as the difference between the desired answer and its guess.</p>
<p>This is also a calculation of an error! The current velocity serves as a guess, and the error (the steering force) indicates how to adjust the velocity in the correct direction. In a moment, you will see how adjusting a vehicle's velocity to follow a target is similar to adjusting the weights of a neural network to arrive at the correct answer.</p>
<p>In the case of the perceptron, the output has only two possible values: <spandata-type="equation">+1</span> or <spandata-type="equation">-1</span>. This means there are only three possible errors.</p>
<p>If the perceptron guesses the correct answer, then the guess equals e the desired output and the error is 0. If the correct answer is -1 and it guessed +1, then the error is -2. If the correct answer is +1 and it guessed -1, then the error is +2.</p>
<table>
<thead>
<tr>
<th>Desired</th>
<th>Guess</th>
<th>Error</th>
</tr>
</thead>
<tbody>
<tr>
<td><spandata-type="equation">-1</span></td>
<td><spandata-type="equation">-1</span></td>
<td><spandata-type="equation">0</span></td>
</tr>
<tr>
<td><spandata-type="equation">-1</span></td>
<td><spandata-type="equation">+1</span></td>
<td><spandata-type="equation">-2</span></td>
</tr>
<tr>
<td><spandata-type="equation">+1</span></td>
<td><spandata-type="equation">-1</span></td>
<td><spandata-type="equation">+2</span></td>
</tr>
<tr>
<td><spandata-type="equation">+1</span></td>
<td><spandata-type="equation">+1</span></td>
<td><spandata-type="equation">0</span></td>
</tr>
</tbody>
</table>
<p>The error is the determining factor in how the perceptron’s weights should be adjusted. For any given weight, what I am looking to calculate is the change in weight, often called <spandata-type="equation">\Delta\text{weight}</span> (or “delta” weight, delta being the Greek letter <spandata-type="equation">\Delta</span>).</p>
<p>To understand why this works, I will again return to steering. A steering force is essentially an error in velocity. By applying a steering force as an acceleration (or <spandata-type="equation">\Delta\text{velocity}</span>), then the velocity is adjusted to move in the correct direction. This is what I want to do with the neural network’s weights. I want to adjust them in the right direction, as defined by the error.</p>
<p>With steering, however, I had an additional variable that controlled the vehicle’s ability to steer: the <em>maximum force</em>. A high maximum force allowed the vehicle to accelerate and turn quickly, while a lower force resulted in a slower velocity adjustment. The neural network will use a similar strategy with a variable called the "learning constant."</p>
<p>Note that a high learning constant causes the weight to change more drastically. This may help the perceptron arrive at a solution more quickly, but it also increases the risk of overshooting the optimal weights. A small learning constant, however, will adjust the weights slowly and require more training time, but allow the network to make small adjustments that could improve overall accuracy.</p>
<p>Assuming the addition of a <code>this.learningConstant</code> property to the <code>Perceptron</code>class, , I can now write a training function for the perceptron following the above steps.</p>
<preclass="codesplit"data-code-language="javascript">// Step 1: Provide the inputs and known answer.
// These are passed in as arguments to train().
train(inputs, desired) {
// Step 2: Guess according to those inputs.
let guess = this.feedforward(inputs);
// Step 3: Compute the error (difference between desired and guess).
let error = desired - guess;
//{!3} Step 4: Adjust all the weights according to the error and learning constant.
for (let i = 0; i < this.weights.length; i++) {
<p>To train the perceptron, I need a set of inputs with a known answer. Now the question becomes, how do I pick a point and know whether it is above or below a line? Let’s start with the formula for a line, where <spandata-type="equation">y</span> is calculated as a function of <spandata-type="equation">x</span>:</p>
<divdata-type="equation">y = f(x)</div>
<p>In generic terms, a line can be described as:</p>
<divdata-type="equation">y = ax + b</div>
<p>Here’s a specific example:</p>
<divdata-type="equation">y = 2x + 1</div>
<p>I can then write a function with this in mind.</p>
<preclass="codesplit"data-code-language="javascript">// A function to calculate y based on x along a line
f(x) {
return 2 * x + 1;
}</pre>
<p>So, if I make up a point:</p>
<preclass="codesplit"data-code-language="javascript">let x = random(width);
let y = random(height);</pre>
<p>How do I know if this point is above or below the line? The line function <spandata-type="equation">f(x)</span> returns <spandata-type="equation">y</span> value on the line for that <spandata-type="equation">x</span> position. Let’s call that <spandata-type="equation">y_\text{line}</span>.</p>
<preclass="codesplit"data-code-language="javascript">// The y position on the line
let yline = f(x);</pre>
<p>If the <spandata-type="equation">y</span> value I am examining is above the line, it will be less than <spandata-type="equation">y_\text{line}</span>.</p>
<figure>
<imgsrc="images/10_nn/10_nn_8.png"alt="Figure 10.8: If y is less than y_\text{line} then it is above the line. Note this is only true for a p5.js canvas where the y axis points down in the positive direction.">
<figcaption>Figure 10.8: If <spandata-type="equation">y</span> is less than <spandata-type="equation">y_\text{line}</span> then it is above the line. Note this is only true for a p5.js canvas where the y axis points down in the positive direction.</figcaption>
</figure>
<preclass="codesplit"data-code-language="javascript">// Start with the value of +1
let desired = 1;
if (y < yline) {
//{!1} The answer is -1 if y is above the line.
desired = -1;
}</pre>
<p>I can then make an inputs array to go with the <code>desired</code> output.</p>
<preclass="codesplit"data-code-language="javascript">// Don't forget to include the bias!
let trainingInputs = [x, y, 1];</pre>
<p>Assuming that I have a <code>perceptron</code> variable, I can train it by providing the inputs along with the desired answer.</p>
<p>Now, it’s important to remember that this is just a demonstration. Remember the Shakespeare-typing monkeys? I asked the genetic algorithm to solve for “to be or not to be”—an answer I already knew. I did this to make sure the genetic algorithm worked properly. The same reasoning applies to this example. I don’t need a perceptron to tell me whether a point is above or below a line; I can do that with simple math. By using an example that I can easily solve without a perceptron, I can both demonstrate the algorithm of the perceptron and verify that it is working properly.</p>
<p>Let’s look the perceptron trained with with an array of many points.</p>
<p></p>
<divdata-type="example">
<h3id="example-101-the-perceptron">Example 10.1: The Perceptron</h3>
<p>Instead of using the supervised learning model above, can you train the neural network to find the right weights by using a genetic algorithm?</p>
</div>
<divdata-type="exercise">
<h3id="exercise-102">Exercise 10.2</h3>
<p>Visualize the perceptron itself. Draw the inputs, the processing node, and the output.</p>
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<h2id="its-a-network-remember">It’s a “Network,” Remember?</h2>
<p>Yes, a perceptron can have multiple inputs, but it is still a lonely neuron. The power of neural networks comes in the networking itself. Perceptrons are, sadly, incredibly limited in their abilities. If you read an AI textbook, it will say that a perceptron can only solve <strong><em>linearly separable</em></strong> problems. What’s a linearly separable problem? Let’s take a look at the first example, which determined whether points were on one side of a line or the other.</p>
<p>On the left of Figure 10.11, is an example of classic linearly separable data. Graph all of the possibilities; if you can classify the data with a straight line, then it is linearly separable. On the right, however, is non-linearly separable data. You can’t draw a straight line to separate the black dots from the gray ones.</p>
<p>One of the simplest examples of a non-linearly separable problem is <em>XOR</em>, or “exclusive or.” By now your should be familiar with <em>AND</em>. For <em>A</em><em>AND</em><em>B</em> to be true, both <em>A</em> and <em>B</em> must be true. With <em>OR</em>, either <em>A</em> or <em>B</em> can be true for <em>A</em><em>OR</em><em>B</em> to evaluate as true. These are both linearly separable problems. Let’s look at the solution space, a “truth table.”</p>
<p>See how you can draw a line to separate the true outputs from the false ones?</p>
<p><em>XOR</em> is the equivalent of <em>OR</em> and <em>NOT AND</em>. In other words, <em>A</em><em>XOR</em><em>B</em> only evaluates to true if one of them is true. If both are false or both are true, then we get false. Take a look at the following truth table.</p>
<p>This is not linearly separable. Try to draw a straight line to separate the true outputs from the false ones—you can’t!</p>
<p>So perceptrons can’t even solve something as simple as <em>XOR</em>. But what if we made a network out of two perceptrons? If one perceptron can solve <em>OR</em> and one perceptron can solve <em>NOT AND</em>, then two perceptrons combined can solve <em>XOR</em>.</p>
<p>The above diagram is known as a <em>multi-layered perceptron</em>, a network of many neurons. Some are input neurons and receive the inputs, some are part of what’s called a “hidden” layer (as they are connected to neither the inputs nor the outputs of the network directly), and then there are the output neurons, from which the results are read.</p>
<p>Training these networks is much more complicated. With the simple perceptron, you could easily evaluate how to change the weights according to the error. But here there are so many different connections, each in a different layer of the network. How does one know how much each neuron or connection contributed to the overall error of the network?</p>
<p>The solution to optimizing weights of a multi-layered network is known as <strong><em>backpropagation</em></strong>. The output of the network is generated in the same manner as a perceptron. The inputs multiplied by the weights are summed and fed forward through the network. The difference here is that they pass through additional layers of neurons before reaching the output. Training the network (i.e. adjusting the weights) also involves taking the error (desired result - guess). The error, however, must be fed backwards through the network. The final error ultimately adjusts the weights of all the connections.</p>
<p>Backpropagation is beyond the scope of this book and involves a fancier activation function (called the sigmoid function) as well as some basic calculus. If you are interested in continuing down this road and learning more about how backpropagation works, you can find <ahref="https://github.com/CodingTrain/Toy-Neural-Network-JS">my “toy neural network” project at github.com/CodingTrain</a> with links to accompanying video tutorials. They go through all the steps of solving <em>XOR</em> using a multi-layered feed forward network with backpropagation. For this chapter, however, I’d like to get some help and phone a friend.</p>
<h2id="machine-learning-with-ml5js">Machine Learning with ml5.js</h2>
<p>That friend is ml5.js. Inspired by the philosophy of p5.js, ml5.js is a JavaScript library that aims to make machine learning accessible to a wide range of artists, creative coders, and students. It is built on top of TensorFlow.js, Google's open-source library that runs machine learning models directly in the browser without the need to install or configure complex environments. However, TensorFlow.js's low-level operations and highly technical API can be intimidating to beginners. That's where ml5.js comes in, providing a friendly entry point for those who are new to machine learning and neural networks.</p>
<p>Before I get to my goal of adding a "neural network" brain to a steering agent and tying ml5.js back into the story of the book, I would like to demonstrate step-by-step how to train a neural network model with "supervised learning." There are several key terms and concepts important to cover, namely “classification”, “regression”, “inputs”, and “outputs”. Examining these ideas within the context of supervised learning scenario is a great way to explore on these foundational concepts, introduce the syntax of the ml5.js library, and tie everything together.</p>
<h3id="classification-and-regression">Classification and Regression</h3>
<p>The majority of machine learning tasks fall into one of two categories: classification and regression. Classification is probably the easier of the two to understand at the start. It involves predicting a “label” (or “category” or “class”) for a piece of data. For example, an “image classifier" might try to guess if a photo is of a cat or a dog and assign the corresponding label.</p>
<p><strong><em>[FIGURE OF CAT OR DOG OR BIRD OR MONKEY OR ILLUSTRATIONS ASSIGNED A LABEL?]</em></strong></p>
<p>This doesn’t happen by magic, however. The model must first be shown many examples of dog and cat illustrations with the correct labels in order to properly configure all the weights of all the connections. This is the supervised learning training process.</p>
<p>The simplest version of this scenario is probably the classic “Hello, World” demonstration of machine learning known as “MNIST”. MNIST, short for 'Modified National Institute of Standards and Technology,' is a dataset that was collected and processed by Yann LeCun and Corinna Cortes (AT&T Labs) and Christopher J.C. Burges (Microsoft Research). It is widely used for training and testing in the field of machine learning and consists of 70,000 handwritten digits from 0 to 9, each digit being a 28x28 pixel grayscale image.</p>
<p><strong><em>[FIGURE FOR MNIST?]</em></strong></p>
<p>While I won't be building a complete MNIST model for training and deployment, it serves as a canonical example of a training dataset for image classification: 70,000 images each assigned one of 10 possible labels. The key element of classification is that the output of the model involves a fixed number of discrete options. There are only 10 possible digits that the model can guess, no more and no less. After the data is used to train the model, the goal is to classify new images and assign the appropriate label.</p>
<p>Regression, on the other hand, is a machine learning task where the prediction is a continuous value, typically a floating point number. A regression problem can involve multiple outputs, but when beginning it’s often simpler to think of it as just one.</p>
<p>Consider a machine learning model that predicts the daily electricity usage of a house based on any number of factors like number of occupants, size of house, temperature outside. Here, rather than a goal of the neural network picking from a discrete set of options, it makes more sense for the neural network to guess a number. Will the house use 30.5 kilowatt-hours of energy that day? 48.7 kWh? 100.2 kWh? The output is therefore a continuous value that the model attempts to predict.</p>
<h3id="inputs-and-outputs">Inputs and Outputs</h3>
<p>Once the task has been determined, the next step is to finalize the configuration of inputs and outputs of the neural network. In the case of MNIST, each image is a collection of 28x28 grayscale pixels and each pixel can be represented as a single value (ranging from 0-255). The total pixels is <spandata-type="equation">28 \times 28 = 784</span>. The grayscale value of each pixel is an input to the neural network.</p>
<figure>
<imgsrc="images/10_nn/10_nn_13.jpg"alt="Place holder figure (just show the inputs first?, borrowed from https://ml4a.github.io/ml4a/looking_inside_neural_nets/">
<figcaption>Place holder figure (just show the inputs first?, borrowed from <ahref="https://ml4a.github.io/ml4a/looking_inside_neural_nets/">https://ml4a.github.io/ml4a/looking_inside_neural_nets/</a></figcaption>
</figure>
<p>Since there are 10 possible digits 0-9, the output of the neural network is a prediction of one of 10 labels.</p>
<p><strong><em>[FIGURE NOW ADDS THE OUTPUTS IN]</em></strong></p>
<p>Let’s consider the regression scenario of predicting the electricity usage of a house. Let’s assume you have a table with the following data:</p>
<p>Here in this table, the inputs to the neural network are the first three columns (occupants, size, temperature). The fourth column on the right is what the neural network is expected to guess, or the output.</p>
<h3id="setting-up-the-neural-network-with-ml5js">Setting up the Neural Network with ml5.js</h3>
<p>In a typical machine learning scenario, the next step after establishing the inputs and outputs is to configure the full architecture of the neural network. This involves specifying the number of hidden layers between the inputs and outputs, the number of neurons in each layer, which activation functions to use, and more! While all of this is technically possible in ml5.js, using a high-level library has the advantage of making its best guesses based on the task, inputs, and outputs to configure the network and so I can get started writing the code itself!</p>
<p>Just as demonstrated with Matter.js and toxiclibs.js in chapter 6, the ml5.js library can be imported into <em>index.html.</em></p>
<p>The ml5.js library is a collection of machine learning models and functions that can be accessed with the syntax <code>ml5.functionName()</code>. If you wanted to use a pre-trained model that detects hands, you might say <code>ml5.handpose()</code> or for classifying images <code>ml5.imageClassifier()</code>. I encourage to explore all of what ml5.js has to offer (and I will reference some of these pre-trained models in upcoming exercise ideas), however, for this chapter, I’ll be focusing on one function only in ml5.js, the function for creating a generic “neural network”: <code>ml5.neuralNetwork()</code>.</p>
<p>Creating the neural network involves first making a JavaScript object with the necessary configuration properties of the network. There are many options you can use to list, but almost all of them are optional as the network will use many defaults. The default task in ml5.js is “regression” so if you wanted to create a neural network for classification you would have to write the code as follows:</p>
let classifier = ml5.neuralNetwork(options);</pre>
<p>This, however, gives ml5.js very little to go on in terms of designing the network architecture. Adding the inputs and outputs will complete the rest of the puzzle for it. In the case of MNIST, we established there were 784 inputs (grayscale pixel colors) and 10 possible output labels (digits “0” through “9”). This can be configured in ml5.js with a single integer for the number of inputs and an array of strings for the list of output labels.</p>
<p>Try incorporating the concept of a “brain” into your creatures.</p>
<ul>
<li>Use reinforcement learning in the creatures’ decision-making process.</li>
<li>Create a creature that features a visualization of its brain as part of its design (even if the brain itself is not functional).</li>
<li>Can the ecosystem as a whole emulate the brain? Can elements of the environment be neurons and the creatures act as inputs and outputs?</li>
</ul>
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<h3id="the-end">The end</h3>
<p>If you’re still reading, thank you! You’ve reached the end of the book. But for as much material as this book contains, we’ve barely scratched the surface of the world we inhabit and of techniques for simulating it. It’s my intention for this book to live as an ongoing project, and I hope to continue adding new tutorials and examples to the book’s website as well as expand and update the printed material. Your feedback is truly appreciated, so please get in touch via email at <code>(daniel@shiffman.net)</code> or by contributing to the GitHub repository, in keeping with the open-source spirit of the project. Share your work. Keep in touch. Let’s be two with nature.</p>
