NanoNeuron – 7 simple JS functions that explain how machines learn


7 easy JavaScript capabilities that provides you with a sense of how machines can truly “be taught”.

You may additionally be desirous about 🤖 Interactive Machine Learning Experiments


NanoNeuron is an over-simplified model of the Neuron idea from Neural Networks. NanoNeuron is skilled to transform temperature values from Celsius to Fahrenheit.

The NanoNeuron.js code instance comprises 7 easy JavaScript capabilities (which touches on mannequin prediction, price calculation, ahead/backwards propagation, and coaching) that provides you with a sense of how machines can truly “be taught”. No Third-party libraries, no exterior data-sets or dependencies, solely pure and easy JavaScript capabilities.

☝🏻These capabilities are NOT, by any means, an entire information to machine studying. Plenty of machine studying ideas are skipped and over-simplified! This simplification is finished on goal to provide the reader a extremely fundamental understanding and feeling of how machines can be taught and finally to make it doable for the reader to acknowledge that it is not “machine studying MAGIC” however quite “machine studying MATH” 🤓.

What our NanoNeuron will be taught

You’ve got in all probability heard about Neurons within the context of Neural Networks. NanoNeuron is simply that however less complicated and we will implement it from scratch. For simplicity causes we’re not even going to construct a community on NanoNeurons. We could have all of it working by itself, doing a little magical predictions for us. Specifically, we are going to educate this singular NanoNeuron to transform (predict) the temperature from Celsius to Fahrenheit.

By the way in which, the components for changing Celsius to Fahrenheit is that this:

Celsius to Fahrenheit

However for now our NanoNeuron would not learn about it…

The NanoNeuron mannequin

Let’s implement our NanoNeuron mannequin operate. It implements fundamental linear dependency between x and y which appears to be like like y = w * x + b. Merely saying our NanoNeuron is a “child” in a “college” that’s being taught to attract the straight line in XY coordinates.

Variables w, b are parameters of the mannequin. NanoNeuron is aware of solely about these two parameters of the linear operate.
These parameters are one thing that NanoNeuron goes to “be taught” through the coaching course of.

The one factor that NanoNeuron can do is to mimic linear dependency. In its predict() technique it accepts some enter x and predicts the output y. No magic right here.

operate NanoNeuron(w, b) {
  this.w = w;
  this.b = b;
  this.predict = (x) => {
    return x * this.w + this.b;

(…wait… linear regression is it you?) 🧐

Celsius to Fahrenheit conversion

The temperature worth in Celsius could be transformed to Fahrenheit utilizing the next components: f = 1.8 * c + 32, the place c is a temperature in Celsius and f is the calculated temperature in Fahrenheit.

operate celsiusToFahrenheit(c) {
  const w = 1.8;
  const b = 32;
  const f = c * w + b;
  return f;

In the end we need to educate our NanoNeuron to mimic this operate (to be taught that w = 1.8 and b = 32) with out figuring out these parameters prematurely.

That is how the Celsius to Fahrenheit conversion operate appears to be like like:

Celsius to Fahrenheit conversion

Producing data-sets

Earlier than the coaching we have to generate coaching and check data-sets primarily based on the celsiusToFahrenheit() operate. Knowledge-sets encompass pairs of enter values and appropriately labeled output values.

In actual life, in most of circumstances, this information can be collected quite than generated. For instance, we’d have a set of photographs of hand-drawn numbers and the corresponding set of numbers that explains what quantity is written on every image.

We’ll use TRAINING instance information to coach our NanoNeuron. Earlier than our NanoNeuron will develop and be capable to make choices by itself, we have to educate it what is true and what’s improper utilizing coaching examples.

We’ll use TEST examples to guage how nicely our NanoNeuron performs on the information that it did not see through the coaching. That is the purpose the place we may see that our “child” has grown and may make choices by itself.

operate generateDataSets() {
  // xTrain -> [0, 1, 2, ...],
  // yTrain -> [32, 33.8, 35.6, ...]
  const xTrain = [];
  const yTrain = [];
  for (let x = 0; x < 100; x += 1) {
    const y = celsiusToFahrenheit(x);

  // xTest -> [0.5, 1.5, 2.5, ...]
  // yTest -> [32.9, 34.7, 36.5, ...]
  const xTest = [];
  const yTest = [];
  // By ranging from 0.5 and utilizing the identical step of 1 as we've used for coaching set
  // we ensure that check set has totally different information evaluating to coaching set.
  for (let x = 0.5; x < 100; x += 1) {
    const y = celsiusToFahrenheit(x);

  return [xTrain, yTrain, xTest, yTest];

The associated fee (the error) of prediction

We have to have some metric that can present us how shut our mannequin’s prediction is to right values. The calculation of the price (the error) between the proper output worth of y and prediction, that our NanoNeuron created, will probably be made utilizing the next components:

Prediction Cost

It is a easy distinction between two values. The nearer the values are to one another, the smaller the distinction. We’re utilizing an influence of 2 right here simply to eliminate damaging numbers in order that (1 - 2) ^ 2 can be the identical as (2 - 1) ^ 2. Division by 2 is occurring simply to simplify additional the backward propagation components (see beneath).

The associated fee operate on this case will probably be so simple as:

operate predictionCost(y, prediction) {
  return (y - prediction) ** 2 / 2; // i.e. -> 235.6

Ahead propagation

To do ahead propagation means to do a prediction for all coaching examples from xTrain and yTrain data-sets and to calculate the typical price of these predictions alongside the way in which.

We simply let our NanoNeuron say its opinion, at this level, by simply permitting it to guess how you can convert the temperature. It could be stupidly improper right here. The typical price will present us how improper our mannequin is true now. This price worth is absolutely essential since altering the NanoNeuron parameters w and b, and by doing the ahead propagation once more; we can consider if our NanoNeuron turned smarter or not after these parameters change.

The typical price will probably be calculated utilizing the next components:

Average Cost

The place m is quite a few coaching examples (in our case: 100).

Right here is how we might implement it in code:

operate forwardPropagation(mannequin, xTrain, yTrain) {
  const m = xTrain.size;
  const predictions = [];
  let price = 0;
  for (let i = 0; i < m; i += 1) {
    const prediction = nanoNeuron.predict(xTrain[i]);
    price += predictionCost(yTrain[i], prediction);
  // We're desirous about common price.
  price /= m;
  return [predictions, cost];

Backward propagation

Once we understand how proper or improper our NanoNeuron’s predictions are (primarily based on common price at this level) what ought to we do to make the predictions extra exact?

The backward propagation offers us the reply to this query. Backward propagation is the method of evaluating the price of prediction and adjusting the NanoNeuron’s parameters w and b in order that subsequent and future predictions can be extra exact.

That is the place the place machine studying appears to be like like magic 🧞‍♂️. The important thing idea right here is the by-product which exhibits what step to take to get nearer to the price operate minimal.

Keep in mind, discovering the minimal of a price operate is the final word objective of the coaching course of. If we discover such values for w and b such that our common price operate will probably be small, it could imply that the NanoNeuron mannequin does actually good and exact predictions.

Derivatives are a giant and separate matter that we’ll not cowl on this article. MathIsFun is an effective useful resource to get a fundamental understanding of it.

One factor about derivatives that can provide help to to know how backward propagation works is that the by-product, by its which means, is a tangent line to the operate curve that factors towards the course of the operate minimal.

Derivative slope

Picture supply: MathIsFun

For instance, on the plot above, you may see that if we’re on the level of (x=2, y=4) then the slope tells us to go left and down to get to the operate minimal. Additionally discover that the larger the slope, the sooner we should always transfer to the minimal.

The derivatives of our averageCost operate for parameters w and b appears to be like like this:



The place m is quite a few coaching examples (in our case: 100).

It’s possible you’ll learn extra about by-product guidelines and how you can get a by-product of complicated capabilities here.

operate backwardPropagation(predictions, xTrain, yTrain) {
  const m = xTrain.size;
  // Initially we do not know through which means our parameters 'w' and 'b' should be modified.
  // Due to this fact we're establishing the altering steps for every parameters to 0.
  let dW = 0;
  let dB = 0;
  for (let i = 0; i < m; i += 1) {
    dW += (yTrain[i] - predictions[i]) * xTrain[i];
    dB += yTrain[i] - predictions[i];
  // We're desirous about common deltas for every params.
  dW /= m;
  dB /= m;
  return [dW, dB];

Coaching the mannequin

Now we all know how you can consider the correctness of our mannequin for all coaching set examples (ahead propagation). We additionally know how you can do small changes to parameters w and b of our NanoNeuron mannequin (backward propagation). However the challenge is that if we run ahead propagation after which backward propagation solely as soon as, it will not be sufficient for our mannequin to be taught any legal guidelines/traits from the coaching information. It’s possible you’ll examine it with attending a someday of elementary college for the child. He/she ought to go to the college not as soon as however day after day and 12 months after 12 months to be taught one thing.

So we have to repeat ahead and backward propagation for our mannequin many occasions. That’s precisely what the trainModel() operate does. It is sort of a “trainer” for our NanoNeuron mannequin:

  • it can spend a while (epochs) with our barely silly NanoNeuron mannequin and attempt to prepare/educate it,
  • it can use particular “books” (xTrain and yTrain data-sets) for coaching,
  • it can push our child to be taught tougher (sooner) by utilizing a studying charge parameter alpha

A number of phrases in regards to the studying charge alpha. That is only a multiplier for dW and dB values we’ve calculated through the backward propagation. So, by-product pointed us towards the course we have to take to discover a minimal of the price operate (dW and dB signal) and it additionally confirmed us how briskly we have to go in that course (absolute values of dW and dB). Now we have to multiply these step sizes to alpha simply to regulate our motion to the minimal sooner or slower. Generally if we use large values for alpha, we’d merely soar over the minimal and by no means discover it.

The analogy with the trainer can be that the tougher s/he pushes our “nano-kid” the sooner our “nano-kid” will be taught but when the trainer pushes too onerous, the “child” could have a nervous breakdown and will not be capable to be taught something 🤯.

Right here is how we will replace our mannequin’s w and b params:



And right here is our coach operate:

operate trainModel({mannequin, epochs, alpha, xTrain, yTrain}) {
  // The is the historical past array of how NanoNeuron learns.
  const costHistory = [];

  // Let's begin counting epochs.
  for (let epoch = 0; epoch < epochs; epoch += 1) {
    // Ahead propagation.
    const [predictions, cost] = forwardPropagation(mannequin, xTrain, yTrain);
    // Backward propagation.
    const [dW, dB] = backwardPropagation(predictions, xTrain, yTrain);
    // Alter our NanoNeuron parameters to extend accuracy of our mannequin predictions.
    nanoNeuron.w += alpha * dW;
    nanoNeuron.b += alpha * dB;

  return costHistory;

Placing all of the items collectively

Now let’s use the capabilities we’ve created above.

Let’s create our NanoNeuron mannequin occasion. At this second the NanoNeuron would not know what values ought to be set for parameters w and b. So let’s arrange w and b randomly.

const w = Math.random(); // i.e. -> 0.9492
const b = Math.random(); // i.e. -> 0.4570
const nanoNeuron = new NanoNeuron(w, b);

Generate coaching and check data-sets.

const [xTrain, yTrain, xTest, yTest] = generateDataSets();

Let’s prepare the mannequin with small incremental (0.0005) steps for 70000 epochs. You’ll be able to play with these parameters, they’re being outlined empirically.

const epochs = 70000;
const alpha = 0.0005;
const trainingCostHistory = trainModel({mannequin: nanoNeuron, epochs, alpha, xTrain, yTrain});

Let’s verify how the price operate was altering through the coaching. We’re anticipating that the price after the coaching ought to be a lot decrease than earlier than. This is able to imply that NanoNeuron received smarter. The alternative can be doable.

console.log('Price earlier than the coaching:', trainingCostHistory[0]); // i.e. -> 4694.3335043
console.log('Price after the coaching:', trainingCostHistory[epochs - 1]); // i.e. -> 0.0000024

That is how the coaching price adjustments over the epochs. On the x axes is the epoch quantity x1000.

Training process

Let’s check out NanoNeuron parameters to see what it has discovered. We count on that NanoNeuron parameters w and b to be just like ones we’ve within the celsiusToFahrenheit() operate (w = 1.8 and b = 32) since our NanoNeuron tried to mimic it.

console.log('NanoNeuron parameters:', {w: nanoNeuron.w, b: nanoNeuron.b}); // i.e. -> {w: 1.8, b: 31.99}

Consider the mannequin accuracy for the check data-set to see how nicely our NanoNeuron offers with new unknown information predictions. The price of predictions on check units is predicted to be near the coaching price. This is able to imply that our NanoNeuron performs nicely on identified and unknown information.

[testPredictions, testCost] = forwardPropagation(nanoNeuron, xTest, yTest);
console.log('Price on new testing information:', testCost); // i.e. -> 0.0000023

Now, since we see that our NanoNeuron “child” has carried out nicely within the “college” through the coaching and that he can convert Celsius to Fahrenheit temperatures appropriately, even for the information it hasn’t seen, we are able to name it “good” and ask him some questions. This was the final word objective of your complete coaching course of.

const tempInCelsius = 70;
const customPrediction = nanoNeuron.predict(tempInCelsius);
console.log(`NanoNeuron "thinks" that ${tempInCelsius}°C in Fahrenheit is:`, customPrediction); // -> 158.0002
console.log('Appropriate reply is:', celsiusToFahrenheit(tempInCelsius)); // -> 158

So shut! As all of us people, our NanoNeuron is sweet however not very best 😃

Blissful studying to you!

The way to launch NanoNeuron

It’s possible you’ll clone the repository and run it regionally:

git clone
cd nano-neuron
node ./NanoNeuron.js

Skipped machine studying ideas

The next machine studying ideas have been skipped and simplified for simplicity of rationalization.

Coaching/testing data-set splitting

Usually you have got one large set of knowledge. Relying on the variety of examples in that set, chances are you’ll need to cut up it in proportion of 70/30 for prepare/check units. The info within the set ought to be randomly shuffled earlier than the cut up. If the variety of examples is large (i.e. tens of millions) then the cut up would possibly occur in proportions which are nearer to 90/10 or 95/5 for prepare/check data-sets.

The community brings the facility

Usually you will not discover the utilization of only one standalone neuron. The facility is within the network of such neurons. The community would possibly be taught way more complicated options. NanoNeuron alone appears to be like extra like a easy linear regression than a neural community.

Enter normalization

Earlier than the coaching, it could be higher to normalize input values.

Vectorized implementation

For networks, the vectorized (matrix) calculations work a lot sooner than for loops. Usually ahead/backward propagation works a lot sooner whether it is applied in vectorized type and calculated utilizing, for instance, Numpy Python library.

Minimal of the price operate

The associated fee operate that we have been utilizing on this instance is over-simplified. It ought to have logarithmic components. Altering the price operate can even change its derivatives so the again propagation step would additionally use totally different formulation.

Activation operate

Usually the output of a neuron ought to be handed by way of an activation operate like Sigmoid or ReLU or others.


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