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Changed name of the examples
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‎NeuralNetworkExample_FishersIris/NeuralNetworkExample_FishersIris.csproj

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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFramework>netcoreapp3.1</TargetFramework>
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</PropertyGroup>
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<ItemGroup>
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<Reference Include="NeuralNetwork">
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<HintPath>..\DLL is HERE\netcoreapp3.1\NeuralNetwork.dll</HintPath>
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</Reference>
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</ItemGroup>
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</Project>

‎NeuralNetworkExample_FishersIris/Program.cs

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using System;
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using System.Globalization;
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using System.IO;
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using System.Reflection;
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using System.Threading;
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using NN;
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namespace NeuralNetworkExample
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{
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//---------------------------------------------------------------
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//
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//
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// This file is just an example how to use the NeuralNetwork library.
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// You are free to use the library in yout own needs
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//
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//
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//---------------------------------------------------------------
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class NeuralNetworkPROBuilder
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{
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const double MomentTemp = 0.7;
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const double LearningRateTemp = 0.1;
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const string NeuronsAndLayers = "4+ 15+ 6 3"; //"[0]-InputNeurons, [1]-Neurons In 1-st HiddenLayer,
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// [2]-Neurons In 2-nd HiddenLayer,[..],[n-1]-Neurons In (n-1)-th HiddenLayer, [n]-OutputNeurons"
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// put + in each layer (except OUTPUT) to add bias
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static double terminatingErrorProcents = 0.001; //The average error procent on which we want to end training
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static uint refreshSpeed = 1800;
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public struct TrainAndTest
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{
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public double[] IN; //All Input values
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public double[] OUT; // All output values
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}
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private static TrainAndTest[] AddTrainOrTest(string fullPath)
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{
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string[] lines = File.ReadAllLines(fullPath);
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//TRAIN or TEST units
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TrainAndTest[] Data = new TrainAndTest[lines.Length / 2];
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//Training and test files are .txt, where non-odd lines are INPUT values and odd lines are OUTPUT values
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uint countOfUnit = 0;
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for (uint i = 1; i <= lines.Length; ++i)
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{
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if (i % 2 != 0)
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{
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string[] line = lines[i - 1].Split(' ');
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Data[countOfUnit].IN = new double[line.Length];
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for (uint j = 0; j < line.Length; ++j) //Every INPUT value in the line must be readen
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Data[countOfUnit].IN[j] = Convert.ToDouble(line[j]);
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}
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else
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{
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string[] line = lines[i - 1].Split(' ');
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Data[countOfUnit].OUT = new double[line.Length];
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for (int j = 0; j < line.Length; ++j) //Every OUTPUT value in the line must be readen
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Data[countOfUnit].OUT[j] = Convert.ToDouble(line[j]);
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++countOfUnit;
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}
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}
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return Data;
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// return Data;
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}
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private static uint CheckForMistakes(ref NeuralNetwork network, ref TrainAndTest[] testData)
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{
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uint errCount = 0;
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for (uint i = 0; i < testData.GetLength(0); ++i) //Run through all TEST units
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{
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Neuron[] answer = network.RunNetwork(testData[i].IN);
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double[] _answer = new double[answer.Length];
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for (uint j = 0; j < answer.Length; ++j) //Normalizing answers in this unit
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_answer[j] = answer[j].value > 0.5 ? 1 : 0;
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for (uint j = 0; j < _answer.Length; ++j) //Checking answers in this unit for a mistake
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{
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if (_answer[j] != testData[i].OUT[j]) //If a mistake was made then:
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{
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for (uint a = 0; a < testData[i].IN.Length; ++a) // -Write down all INPUTs
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Console.Write($"I{a}={testData[i].IN[a]} ");
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Console.WriteLine();
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for (uint k = 0; k < answer.Length; ++k) // -Write down all OUTPUTS + activated OUTPUTS + ideal OUTPUTS
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Console.WriteLine("Real answer = " + answer[k].value + " Activated answer = " + _answer[k] + " Ideal = " + testData[i].OUT[k]);
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++errCount;
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Console.WriteLine();
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break;
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}
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}
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}
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return errCount;
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}
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private static bool pressedENTER = false;
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private static void CheckForEnter()
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{
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while (!pressedENTER)
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{
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if (Console.KeyAvailable && Console.ReadKey(true).Key == ConsoleKey.Enter)
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{
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pressedENTER = true;
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return;
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}
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}
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}
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static void Main()
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{
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var sepByThous = (NumberFormatInfo)CultureInfo.InvariantCulture.NumberFormat.Clone();
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sepByThous.NumberGroupSeparator = " ";
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#region Training&Test initialization
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string firstPath = Path.GetDirectoryName(Assembly.GetExecutingAssembly().Location);
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firstPath = Path.Combine(AppContext.BaseDirectory, @"..\..\..\TrainingAndTest\");
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TrainAndTest[] trainingData = AddTrainOrTest(firstPath + "numbers.txt");
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//The same for TEST units
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TrainAndTest[] testData = AddTrainOrTest(firstPath + "numbersTEST.txt");
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#endregion
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#region Main part - network training
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new Thread(new ThreadStart(CheckForEnter)).Start();
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//Creating an object of NeuralNetwork with same parameters as we described in variables
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//NeuralNetwork network = new NeuralNetwork(@"C:\s\Neural.aaa");
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NeuralNetwork network = new NeuralNetwork(NeuronsAndLayers, -1, 1)
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{
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Moment = MomentTemp,
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LearningRate = LearningRateTemp
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};
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System.Diagnostics.Stopwatch sw = new System.Diagnostics.Stopwatch();
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sw.Start();
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double errorSum;
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uint iteration = 0;
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uint i;
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uint j;
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double end;
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double error;
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Neuron[] endValue;
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do
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{
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errorSum = 0;
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for (i = 0; i < trainingData.Length; ++i) //Run through all TRAIN units
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{
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//Running the network with current INPUT values of this unit
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endValue = network.RunNetwork(trainingData[i].IN);
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//Counting an error of current unit
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end = 0;
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for (j = 0; j < endValue.Length; ++j)
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end += Math.Pow(trainingData[i].OUT[j] - endValue[j].value, 2);
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error = end / trainingData.Length; //((i-a1)*(i1-a1)+...+(in-an)*(in-an))/n
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errorSum += error;
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network.TeachNetwork(trainingData[i].OUT);
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}
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if (iteration++ % refreshSpeed == 0)
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Console.WriteLine(iteration.ToString("#,0", sepByThous) + " average error = " + Math.Round((errorSum / trainingData.GetLength(0)) * 100, 5) + "%" + " = " + Math.Round(errorSum * 100, 5) + "% general, " + ((double)sw.ElapsedMilliseconds / 1000).ToString("#,0.000", sepByThous) + " sec");
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} while (((errorSum / trainingData.GetLength(0)) * 100 > terminatingErrorProcents) && !pressedENTER); //while average error procent is greater tnah TEP - continue
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sw.Stop();
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#endregion
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#region Output
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uint errCount = CheckForMistakes(ref network, ref testData);
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Console.WriteLine("\nAccuracy = " + Math.Round((double)(testData.GetLength(0) - (errCount)) / testData.GetLength(0) * 100, 3) + "%");
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Console.WriteLine("Right answers from the test = " + (testData.GetLength(0) - errCount).ToString("#,0", sepByThous) + " of " + testData.GetLength(0).ToString("#,0", sepByThous));
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Console.WriteLine("\nNumber of iterations = " + iteration.ToString("#,0", sepByThous));
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Console.WriteLine("Training time = " + ((double)sw.ElapsedMilliseconds / 1000).ToString("#,0.000", sepByThous) + " sec");
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//network.SaveNetwork(@"C:\s\Neural.aaa");
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#endregion
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}
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}
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}

‎NeuralNetworkExample_FishersIris/TrainingAndTest/TrainingPhoto.png

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