example1.java

All the tool for build a network able to reconize any shapes. Very complete, a good base for anythi

package neuralNetwork;
/*
Coded by Aydin Gurel, 2003
The code is free, but please contact me if you wish to use the code entirely or partially in any kind of project so that I can reference it and please don't delete these lines so that other people can reach this information. Also, please inform me if you encounter a bug.
[e-mail]
http://aydingurel.brinkster.com/neural

The purpose of this example is to show you how this package should be used, rather than to give you clues about how to train and use a neural network in general.

In this example, we:
-create a generalized feed forward net using a previously saved configuration,
-create a patternset, reserve 10% of it for cross validation, 10% for testing and the rest for training
-train it using batch training until crossvalerror < 0.02 so that it learns how to compute a simple function such as y = x1 + x2,
-save its weights,
-recreate the net using previously saved configuration and weigts,
-test it
*/

import java.io.*;


public class example1 {
	public static void main(String args[]) {

		//  construct a Randomizer object using a seed
		Randomizer randomizer = new Randomizer(4);

		// create a net using a configuration file
		System.out.println("Creating the net");
		NeuralNet mynet = new NeuralNet("example1.nnc", randomizer);

		// create a pattern set with 2 input and 1 output values.
		// randomly choose 80% of data for training, 10% for cross validation, 10% for testing.
		// the function to be learned is: y = x1 + x2
		PatternSet mypatterns = new PatternSet("example1.csv", 2, 1, .8, .1, .1, randomizer);

		// display the error rate before training
		System.out.println("\n\nError ratio before training: " + mynet.CrossValErrorRatio(mypatterns) );

		// train the net using batch training, until error ratio < 0.02
		while ( mynet.CrossValErrorRatio(mypatterns) > 0.002 ) {
			mynet.BatchTrainPatterns(mypatterns.trainingpatterns, .8);
			System.out.println("Training the net. Error ratio: " + mynet.CrossValErrorRatio(mypatterns) );
		}
		
		// check the error using test data
		System.out.println("Error ratio of the test data: " + mynet.TestErrorRatio(mypatterns) );
		
		System.out.println("Training is over");
		// note that this was the easiest function to learn. if we had chosen another function
		// instead of y = x1 + x2, we would need much more training.
		
		// now that the training is over, save the weights of the net.
		System.out.println("Saving the weights\n");
		try{mynet.SaveWeights("example1.nnw");}catch(IOException e){}

		// clean up the objects
		mypatterns = null;
		mynet = null;
		randomizer = null;
		
		///痋_/痋_/痋_/痋_/痋_/痋_/痋_/痋_/痋_/痋_/痋_/痋_/痋_/痋_/痋_/痋_/
		
		// now recreate the net using previously saved data and
		// use the trained net to calculate some numbers.
		// You could use this part in a separate java class.
		
		// recreate the net
		randomizer = new Randomizer();
		System.out.println("Recreating the net");
		mynet = new NeuralNet("example1.nnc", randomizer);
		mynet.LoadWeights("example1.nnw");
		
		// and test it
		double[] inputs = {-.5, .25};
		double[] outputs = mynet.Output(inputs); // Although there will be only one output.
		System.out.println("-0.5 + 0.25 = " + outputs[0]);

	}
}