[Solved] INT301 Lab 5-Further Practice with Perceptrons

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Demo: classifying 3D patterns into 2 classes

The following Matlab program sets up a perceptron network with 3 input units and 1 output unit to perform a 2-way classification:

initPerceptron.m plotPerceptronInput.m plotDecisionSurf.m trainPerceptron.m runPerceptron.m

(initialization)(plot the input patterns)(plot the classification boundary)(train perceptron with implemented learning algorithm) (run the perceptron training and other related m-files)

1. 1) The m-file script initPerceptron.m initializes the weights and the training patterns;
2. 2) The m-file script runPerceptron.m calls the function trainPerceptron and plots the input patterns and the classification boundary (hyperplane) after 1 learning epoch (use all the data once);
3. 3) The patterns displayed as asterisks have a target output of +1; the patterns displayed as circles have a target output of -1.

Exercises:1. Run the exampleRead the code and understand how the weights and states are initialized and how the weights are updated. Then do the following:

1) Execute the script initPerceptron, which initializes the weights and training patterns;

2. 2) Run the script runPerceptron (it is suggested to adjust the positions of Matlab figure window and command windows so they are both visible);
3. 3) Repeatedly run the script runPerceptron until the network has converged (weight no longer changes). You can drag the figure to adjust the viewing angle. The classification boundary can be observed in the figure and the numerical values of the weights is printed out in command window after each runPerceptron call. It may take between 5 and 20 learning epochs (run runPerceptron for 5-20 times) before the learning converges.

Repeat this entire process for several trials: for each trial, re-initialize the perceptron (initPerceptron) and re-train (runPerceptron) until convergence. Think about the reason of different convergence time for each trial.

2. Increase the classification difficulty

You can change the variance of the random Gaussian function used to generate different training patterns. Specifically, change var in initPerceptron from 0.1 to 0.3. Re-run your Perceptron a few more times on these data.

1) Does the Perceptron converge more quickly or more slowly on these data?2) After each time you re-initialize the perceptron and data set, does it always

converge, or sometimes fail to converge?3) How can you explain the convergence or lack of convergence by looking at