ML Testing

A Perceptron must be Tested and Evaluated.

A Perceptron must be tested against Real Values.

Test Your Library

Generate new unknown points and check if your Perceptron can guess the right answers:

Example

// Test Against Unknown Data
const counter = 500;
for (let i = 0; i < counter; i++) {
  let x = Math.random() * xMax;
  let y = Math.random() * yMax;
  let guess = ptron.activate([x, y, ptron.bias]);
  let color = "black";
  if (guess == 0) color = "blue";
  plotter.plotPoint(x, y, color);
}

Count the Errors

Add a counter to count the number of errors:

Example

// Test Against Unknown Data
const counter = 500;
let errors = 0;
for (let i = 0; i < counter; i++) {
  let x = Math.random() * xMax;
  let y = Math.random() * yMax;
  let guess = ptron.activate([x, y, ptron.bias]);
  let color = "black";
  if (guess == 0) color = "blue";
  plotter.plotPoint(x, y, color);
  if ((y > f(x) && guess == 0) || (y < f(x) && guess == 1)) {errors++}
}

Tune the Perceptron

How can you tune the Perceptron?

Here are some suggestions:

• Adjust the learning rate
• Increase the number of training data
• Increase the number of training iterations