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Training a Perceptron

  • Create a Perceptron Object
  • Create a Training Function
  • Train the perceptron against the desired answers

Training Task

Imagine a straight line in a space with scattered x y points.

Train a perceptron to classify the points over and under the line.


Create a Perceptron Object

Create a Perceptron object. Name it anything (like Perceptron).

Let the perceptron accept two parameters:

  1. The number of inputs (no)
  2. The learning rate (learningRate).

Set the default learning rate to 0.00001.

Then create random weights between -1 and 1 for each input.

Example

// Perceptron Object
function Perceptron(no, learningRate = 0.00001) {

// Set Initial Values
this.learnc = learningRate;
this.bias = 1;

// Compute Random Weights
this.weights = [];
for (let i = 0; i <= no; i++) {
  this.weights[i] = Math.random() * 2 - 1;
}

// End Perceptron Object
}

The Random Weights

The Perceptron will start with a random weight for each input.

The Learning Rate

For each mistake, while training the Perceptron, the weights will be ajusted with a small fraction.

This small fraction is the "Perceptron's learning rate".

In the Perceptron object we call it learnc.

The Bias

Sometimes, if both inputs are zero, the perceptron might produce an in correct output.

To avoid this, we give the perceptron an extra input with the value of 1.

This is called a bias.


Add an Activate Function

Remember the perceptron algorithm:

  • Multiply each input with the perceptron's weights
  • Sum the results
  • Compute the outcome

Example

this.activate = function(inputs) {
  let sum = 0;
  for (let i = 0; i < inputs.length; i++) {
    sum += inputs[i] * this.weights[i];
  }
  if (sum > 0) {return 1} else {return 0}
}

The activation function will output:

  • 1 if the sum is greater than 0
  • 0 if the sum is less than 0

Create a Training Function

The training function guesses the outcome based on the activate function.

Every time the guess is wrong, the perceptron should adjust the weights.

After many guesses and adjustments, the weights will be correct.

Example

this.train = function(inputs, desired) {
  inputs.push(this.bias);
  let guess = this.activate(inputs);
  let error = desired - guess;
  if (error != 0) {
    for (let i = 0; i < inputs.length; i++) {
      this.weights[i] += this.learnc * error * inputs[i];
    }
  }
}

Try it Yourself »


Backpropagation

After each guess, the perceptron calculates how wrong the guess was.

If the guess is wrong, the perceptron adjusts the bias and the weights so that the guess will be a little bit more correct the next time.

This type of learning is called backpropagation.

After trying (a few thousand times) your perceptron will become quite good at guessing.


Create Your Own Library

Library Code

// Perceptron Object
function Perceptron(no, learningRate = 0.00001) {

// Set Initial Values
this.learnc = learningRate;
this.bias = 1;

// Compute Random Weights
this.weights = [];
for (let i = 0; i <= no; i++) {
  this.weights[i] = Math.random() * 2 - 1;
}

// Activate Function
this.activate = function(inputs) {
  let sum = 0;
  for (let i = 0; i < inputs.length; i++) {
    sum += inputs[i] * this.weights[i];
  }
  if (sum > 0) {return 1} else {return 0}
}

// Train Function
this.train = function(inputs, desired) {
  inputs.push(this.bias);
  let guess = this.activate(inputs);
  let error = desired - guess;
  if (error != 0) {
    for (let i = 0; i < inputs.length; i++) {
      this.weights[i] += this.learnc * error * inputs[i];
    }
  }
}

// End Perceptron Object
}

Now you can include the library in HTML:

<script src="myperceptron.js"></script>

Use Your Library

Example

// Initiate Values
const numPoints = 500;
const learningRate = 0.00001;

// Create a Plotter
const plotter = new XYPlotter("myCanvas");
plotter.transformXY();
const xMax = plotter.xMax;
const yMax = plotter.yMax;
const xMin = plotter.xMin;
const yMin = plotter.yMin;

// Create Random XY Points
const xPoints = [];
const yPoints = [];
for (let i = 0; i < numPoints; i++) {
  xPoints[i] = Math.random() * xMax;
  yPoints[i] = Math.random() * yMax;
}

// Line Function
function f(x) {
  return x * 1.2 + 50;
}

//Plot the Line
plotter.plotLine(xMin, f(xMin), xMax, f(xMax), "black");

// Compute Desired Answers
const desired = [];
for (let i = 0; i < numPoints; i++) {
  desired[i] = 0;
  if (yPoints[i] > f(xPoints[i])) {desired[i] = 1}
}

// Create a Perceptron
const ptron = new Perceptron(2, learningRate);

// Train the Perceptron
for (let j = 0; j <= 10000; j++) {
  for (let i = 0; i < numPoints; i++) {
    ptron.train([xPoints[i], yPoints[i]], desired[i]);
  }
}

// Display the Result
for (let i = 0; i < numPoints; i++) {
  const x = xPoints[i];
  const y = yPoints[i];
  let guess = ptron.activate([x, y, ptron.bias]);
  let color = "black";
  if (guess == 0) color = "blue";
  plotter.plotPoint(x, y, color);
}

Try it Yourself »