Introduction to Neural Networks for C#, Session 6

Session Material

I present here my solution for program 1. You can use the link below to download an Visual Studio project for my solution.

[My Solution for Program 1]

First you will need to create training data in a CSV file for Program 1.

Listing: CSV Input for Program 1

0,0,0,1,0,0
0,1,0,1,0,0
1,0,0,1,0,0
1,1,1,1,0,0
0,0,0,0,1,0
0,1,1,0,1,0
1,0,1,0,1,0
1,1,1,0,1,0
0,0,0,0,0,1
0,1,1,0,0,1
1,0,1,0,0,1
1,1,0,0,0,1

Solution for program 1.

Listing: Solution for Program 1

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using HeatonResearchNeural.Feedforward;
using HeatonResearchNeural.Feedforward.Train;
using HeatonResearchNeural.Feedforward.Train.Backpropagation;
using HeatonResearchNeural.Util;
using System.Threading;

namespace Program1
{
    class Program1
    {

        const int COLUMNS = 6;
        const int OPP_AND = 0;
        const int OPP_OR = 1;
        const int OPP_XOR = 2;

        private FeedforwardNetwork network;
        private double[][] input;
        private double[][] ideal;

        public void CreateNetwork()
        {
            this.network = new FeedforwardNetwork();
            network.AddLayer(new FeedforwardLayer(Program1.COLUMNS));
            network.AddLayer(new FeedforwardLayer(3));
            network.AddLayer(new FeedforwardLayer(1));
            network.Reset();
        }

        public void Train()
        {
            // train the neural network
            Train train = new Backpropagation(network, this.input,
                    this.ideal,
                    0.7, 0.8);

            int epoch = 1;

            do
            {
                train.Iteration();
                Console.WriteLine("Epoch #" + epoch + " Error:" + train.Error);
                epoch++;
            } while ((epoch < 5000) && (train.Error > 0.001));
        }

        public double Evaluate(double op1, double op2, int operation)
        {
            double[] input = new double[Program1.COLUMNS];
            input[0] = op1;
            input[1] = op2;

            // AND
            if (operation == Program1.OPP_AND)
                input[2] = 1.0;
            else
                input[2] = 0.0;

            // OR
            if (operation == Program1.OPP_OR)
                input[3] = 1.0;
            else
                input[3] = 0.0;

            // XOR
            if (operation == Program1.OPP_XOR)
                input[4] = 1.0;
            else
                input[4] = 0.0;

            double[] output = this.network.ComputeOutputs(input);
            return output[0];
        }

        public void Load(String filename)
        {
            int size = 0;

            // count the size of the file
            ReadCSV csv = new ReadCSV(filename, false);
            while (csv.Next())
            {
                size++;
            }
            csv.Close();

            // allocate enough space
            this.input = new double[size][];
            this.ideal = new double[size][];

            // now load it
            int index = 0;
            csv = new ReadCSV(filename, false);
            while (csv.Next())
            {
                this.input[index] = new double[Program1.COLUMNS];
                this.ideal[index] = new double[1];
                this.input[index][0] = Double.Parse(csv.Get(0));
                this.input[index][1] = Double.Parse(csv.Get(1));
                this.input[index][2] = Double.Parse(csv.Get(3));
                this.input[index][3] = Double.Parse(csv.Get(4));
                this.input[index][4] = Double.Parse(csv.Get(5));
                this.ideal[index][0] = Double.Parse(csv.Get(2));
                index++;
            }
            csv.Close();
        }


        static void Main(string[] args)
        {
            Program1 prg = new Program1();
            prg.Load("c:\\data\\logic.csv");
            prg.CreateNetwork();
            prg.Train();
            Console.WriteLine("1 AND 1 = " + prg.Evaluate(1, 1, Program1.OPP_AND));
            Console.WriteLine("1 XOR 1 = " + prg.Evaluate(1, 1, Program1.OPP_XOR));
            Console.WriteLine("1 AND 0 = " + prg.Evaluate(1, 0, Program1.OPP_AND));
            Console.WriteLine("1 OR 1 = " + prg.Evaluate(1, 1, Program1.OPP_OR));
        }
    }
}

Videos for this Session

Video	Title
	Introduction to Neural Networks for C#(Class 6)