static double[][] MakeAllData(int numInput, int numHidden, int numOutput, int numRows, int seed) { Random rnd = new Random(seed); int numWeights = (numInput * numHidden) + numHidden + (numHidden * numOutput) + numOutput; double[] weights = new double[numWeights]; // actually weights & biases for (int i = 0; i < numWeights; ++i) { weights[i] = 20.0 * rnd.NextDouble() - 10.0; // [-10.0 to 10.0] } Console.WriteLine("Generating weights and biases:"); ShowVector(weights, 2, 10, true); double[][] result = new double[numRows][]; // allocate return-result for (int i = 0; i < numRows; ++i) { result[i] = new double[numInput + numOutput]; // 1-of-N in last column } NeuralNetwork gnn = new NeuralNetwork(numInput, numHidden, numOutput); // generating NN gnn.SetWeights(weights); for (int r = 0; r < numRows; ++r) // for each row { // generate random inputs double[] inputs = new double[numInput]; for (int i = 0; i < numInput; ++i) { inputs[i] = 20.0 * rnd.NextDouble() - 10.0; // [-10.0 to -10.0] } // compute outputs double[] outputs = gnn.ComputeOutputs(inputs); // translate outputs to 1-of-N double[] oneOfN = new double[numOutput]; // all 0.0 int maxIndex = 0; double maxValue = outputs[0]; for (int i = 0; i < numOutput; ++i) { if (outputs[i] > maxValue) { maxIndex = i; maxValue = outputs[i]; } } oneOfN[maxIndex] = 1.0; // place inputs and 1-of-N output values into curr row int c = 0; // column into result[][] for (int i = 0; i < numInput; ++i) // inputs { result[r][c++] = inputs[i]; } for (int i = 0; i < numOutput; ++i) // outputs { result[r][c++] = oneOfN[i]; } } // each row return(result); } // MakeAllData
static void Main(string[] args) { Console.WriteLine("\nBegin neural network back-propagation demo"); int numInput = 86; // number features 5 int numHidden = 30; //4 int numOutput = 1; // number of classes for Y //2 int numRows = 1000; int seed = 1; // gives nice demo //Console.WriteLine("\nGenerating " + numRows + // " artificial data items with " + numInput + " features"); //double[][] allData = MakeAllData(numInput, numHidden, numOutput, // numRows, seed); double[][] allData = GetFeatures(); Console.WriteLine("Done"); //ShowMatrix(allData, allData.Length, 2, true); Console.WriteLine("\nCreating train (80%) and test (20%) matrices"); double[][] trainData; double[][] testData; SplitTrainTest(allData, 0.80, seed, out trainData, out testData); Console.WriteLine("Done\n"); Console.WriteLine("Training data:"); ShowMatrix(trainData, 4, 2, true); Console.WriteLine("Test data:"); ShowMatrix(testData, 4, 2, true); Console.WriteLine("Creating a " + numInput + "-" + numHidden + "-" + numOutput + " neural network"); NeuralNetwork nn = new NeuralNetwork(numInput, numHidden, numOutput); int maxEpochs = 1000; double learnRate = 0.05; double momentum = 0.01; Console.WriteLine("\nSetting maxEpochs = " + maxEpochs); Console.WriteLine("Setting learnRate = " + learnRate.ToString("F2")); Console.WriteLine("Setting momentum = " + momentum.ToString("F2")); Console.WriteLine("\nStarting training"); double[] weights = nn.Train(trainData, maxEpochs, learnRate, momentum); Console.WriteLine("Done"); Console.WriteLine("\nFinal neural network model weights and biases:\n"); ShowVector(weights, 2, 10, true); //double[] y = nn.ComputeOutputs(new double[] { 1.0, 2.0, 3.0, 4.0 }); //ShowVector(y, 3, 3, true); double trainAcc = nn.Accuracy(trainData); Console.WriteLine("\nFinal accuracy on training data = " + trainAcc.ToString("F4")); double testAcc = nn.Accuracy(testData); Console.WriteLine("Final accuracy on test data = " + testAcc.ToString("F4")); Console.WriteLine("\nEnd back-propagation demo\n"); //SerializeObject<NeuralNetwork>(nn, @"C:\DEV\MachineLearning\SBERBANK\NN.xml"); //NeuralNetwork nn1 = DeSerializeObject<NeuralNetwork>(@"C:\DEV\MachineLearning\SBERBANK\serial_NN_XML.xml"); //var item = nn1.GetWeights(); //for (int i = 0; i < item.Length; i++) //{ // Console.Write(item[i] + " | "); //} Console.ReadLine(); } // Main