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AI•Machine Learning

Training a Perceptron

Create a

Perceptron Object

Create a

Training Function

Train the perceptron against correct 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.

Click to Train Me

Create a Perceptron Object

Create a Perceptron object. Name it anything (like Perceptron). Let the perceptron accept two parameters: The number of inputs (no) The learning rate (learningRate). Set the default learning rate to 0.00001.

Formula

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 adjusted 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 incorrect 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];
}
}
}

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

Pattern Recognition

Testing a Perceptron

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Learn/AI/Machine Learning

AI•Machine Learning

Training a Perceptron

Create a

Perceptron Object

Create a

Training Function

Train the perceptron against correct 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.

Click to Train Me

Create a Perceptron Object

Formula

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 adjusted 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 incorrect 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

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];
}
}
}

Backpropagation

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

Pattern Recognition

Testing a Perceptron