example2.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:
-aim to resolve a classification problem,
-create a multilayer perceptron,
-save the configuration of the net,
-create three patternsets;
-one for training, one for cross validation and one for testing.
-train the net using mini batch, until crossvalerror < 0.02, so that it learns how to distinguish a female crab by using some measurements,
-save its weights,
-recreate the net using previously saved configuration and weigts,
-test it

training data was taken from a tutorial of the "NeuroSolutions" software
http://www.neurosolutions.com/
*/

import java.io.*;

  
public class example2 {
	public static void main(String args[]) {

		Randomizer randomizer = new Randomizer();

		// create a multilayer perceptron with four layers:
		// one input layer with seven units; two hidden layers 
		// each with ten units, using tanh function;
		// one output layer with one unit using tanh function.
		// except for noofneurons, all parameters for the input layer
		// are ineffectual.
		int[] noofneurons = {7,10,10,1};
		double[] learnratecoeff = {1, 1, 1, 1};
		char[] axonfamily = {'t', 't', 't', 't'};
		double[] momentumrate = {0, .6, .5, .4};
		double[] flatness = {1, 1.2, 1.1, 1};
		System.out.println("Creating the net");
		NeuralNet mynet = new NeuralNet(noofneurons, learnratecoeff, axonfamily, momentumrate, flatness, randomizer);
		// Save the configuration to a file
		System.out.println("Saving the configuration");
		try{mynet.SaveConfig("example2.nnc");}catch(IOException e){}

		// create three pattern sets with 7 input and 1 output values.
		System.out.println("Loading patterns");
		// first create a pattern set for training
		PatternSet trainingpatterns = new PatternSet("example2_training.csv", 7, 1, 1, 0, 0, randomizer);
		// then create a pattern set for cross validation
		PatternSet crossvalpatterns = new PatternSet("example2_crossval.csv", 7, 1, 0, 1, 0, randomizer);
		// and then create a pattern set for testing
		PatternSet testpatterns = new PatternSet("example2_test.csv", 7, 1, 0, 0, 1, randomizer);

		// show the error ratio before training
		System.out.println("Error ratio before training: " + mynet.CrossValErrorRatio(crossvalpatterns) );

		// train the net using mini batch training
		System.out.println("Beginning mini batch training");
		double temp_err;
		temp_err = mynet.CrossValErrorRatio(crossvalpatterns);
		while (temp_err > .02) {
			System.out.println("Training the net. Error ratio: " + temp_err );
			mynet.MinibatchTrainPatterns(trainingpatterns.trainingpatterns, .1, 20);
			temp_err = mynet.CrossValErrorRatio(crossvalpatterns);
		}

		/*
		// or you can use incremental training:
		System.out.println("Beginning incremental training");
		double temp_err;
		temp_err = mynet.CrossValErrorRatio(crossvalpatterns);
		while (temp_err > .02) {
			System.out.println("Training the net. Error ratio: " + temp_err );
			mynet.IncrementalTrainPatterns(trainingpatterns.trainingpatterns, .01);
			temp_err = mynet.CrossValErrorRatio(crossvalpatterns);
		}
		*/

		// finally, check the error using test data
		System.out.println("Error ratio of the test data: " + mynet.TestErrorRatio(testpatterns) );
		
		System.out.println("Training is over");
		
		// now that the training is over, save the weights of the net.
		System.out.println("Saving the weights\n");
		try{mynet.SaveWeights("example2.nnw");}catch(IOException e){}

		// clean up the objects
		trainingpatterns = null;
		crossvalpatterns = null;
		testpatterns = 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
		// test it.
		
		// recreate the net
		randomizer = new Randomizer();
		System.out.println("Recreating the net");
		mynet = new NeuralNet("example2.nnc", randomizer);
		mynet.LoadWeights("example2.nnw");
		
		// and test it
		double[] inputs = {-19.5,0.5,-0.683,0.4615,-2.0055,-0.8145,-2.0305};
		System.out.println("Feeding the net using data taken from a female crab");
		double[] outputs = mynet.Output(inputs);
		if ( outputs[0] < 0 ) {
			System.out.println("Result: Male");
		}
		else {
			System.out.println("Result: Female");
		}

		mynet = null;
		
	}
}