Example 3: Perceptron Learning
in 2-d pattern space
The following applet further
demonstrates supervised learning using the perceptron learning
algorithm. Using this applet, you can train the perceptron
to learn in a 2-d pattern space. By placing two distinct patterns
of red dots and blue dots, the perceptron can learn to
distinguish between the two.
Instructions:
To Train The Perceptron:
Select the desired number of red and blue dots by
clicking the radio buttons provided.
Click on the white box on the left fo the applet to place
red dots. To place blue dots do the same but hold down
the <Shift> key.
Adjust the training parameters as desired. Legal values
are as follows:
Learning Rate: 0.0 to 1.0
Iterations: 1 to 10000
Error Threshold: 0.0 to 0.5
Click the Train button to begin a normal
training session OR
Click the Step button repeatedly to
single-step through the training session.
During Training:
To stop a training session (normal) in progress, click
the Stop button.
A few notes:
The progress of the training session is displayed
in the information field at the bottom of the
screen.
The error for the current input vector is
displayed in the Current-Error text
field.
The perceptron's ability to classify the inputs
into two classes (0(red) and 1(blue)) is shown in
a graph on the pattern space.
Some useful info:
To clear the Total error list and the pattern space click
Clear.
To plot a graph showing the results of training click Plot.