Ejemplo n.º 1
0
        static void Main(string[] args)
        {
            try
              {
            Console.WriteLine("\nBegin Neural Network training using Back-Propagation demo\n");

            Random rnd = new Random(1); // for random weights. not used.

            double[] xValues = new double[3] { 1.0, -2.0, 3.0 }; // inputs
            double[] yValues; // outputs
            double[] tValues = new double[2] { 0.1234, 0.8766 }; // target values

            Console.WriteLine("The fixed input xValues are:");
            Helpers.ShowVector(xValues, 1, 8, true);

            Console.WriteLine("The fixed target tValues are:");
            Helpers.ShowVector(tValues, 4, 8, true);

            int numInput = 3;
            int numHidden = 4;
            int numOutput = 2;
            int numWeights = (numInput * numHidden) + (numHidden * numOutput) + (numHidden + numOutput);

            Console.WriteLine("Creating a " + numInput + "-input, " + numHidden + "-hidden, " + numOutput + "-output neural network");
            Console.WriteLine("Using hard-coded tanh function for hidden layer activation");
            Console.WriteLine("Using hard-coded log-sigmoid function for output layer activation");

            BackPropNeuralNet bnn = new BackPropNeuralNet(numInput, numHidden, numOutput);

            //Console.WriteLine("\nGenerating random initial weights and bias values");
            //double[] initWeights = new double[numWeights];
            //for (int i = 0; i < initWeights.Length; ++i)
            //  initWeights[i] = (0.1 - 0.01) * rnd.NextDouble() + 0.01;

            Console.WriteLine("\nCreating arbitrary initial weights and bias values");
            double[] initWeights = new double[26] {
              0.001, 0.002, 0.003, 0.004,
              0.005, 0.006, 0.007, 0.008,
              0.009, 0.010, 0.011, 0.012,

              0.013, 0.014, 0.015, 0.016,

              0.017, 0.018,
              0.019, 0.020,
              0.021, 0.022,
              0.023, 0.024,

              0.025, 0.026 };

            Console.WriteLine("\nInitial weights and biases are:");
            Helpers.ShowVector(initWeights, 3, 8, true);

            Console.WriteLine("Loading neural network initial weights and biases into neural network");
            bnn.SetWeights(initWeights);

            double learnRate = 0.5;  // learning rate - controls the maginitude of the increase in the change in weights.
            double momentum = 0.1; // momentum - to discourage oscillation.
            Console.WriteLine("Setting learning rate = " + learnRate.ToString("F2") + " and momentum = " + momentum.ToString("F2"));

            int maxEpochs = 10000;
            double errorThresh = 0.00001;
            Console.WriteLine("\nSetting max epochs = " + maxEpochs + " and error threshold = " + errorThresh.ToString("F6"));

            int epoch = 0;
            double error = double.MaxValue;
            Console.WriteLine("\nBeginning training using back-propagation\n");

            while (epoch < maxEpochs) // train
            {
              if (epoch % 20 == 0) Console.WriteLine("epoch = " + epoch);

              yValues = bnn.ComputeOutputs(xValues);
              error = Helpers.Error(tValues, yValues);
              if (error < errorThresh)
              {
            Console.WriteLine("Found weights and bias values that meet the error criterion at epoch " + epoch);
            break;
              }
              bnn.UpdateWeights(tValues, learnRate, learnRate);
              ++epoch;
            } // train loop

            double[] finalWeights = bnn.GetWeights();
            Console.WriteLine("");
            Console.WriteLine("Final neural network weights and bias values are:");
            Helpers.ShowVector(finalWeights, 5, 8, true);

            yValues = bnn.ComputeOutputs(xValues);
            Console.WriteLine("\nThe yValues using final weights are:");
            Helpers.ShowVector(yValues, 8, 8, true);

            double finalError = Helpers.Error(tValues, yValues);
            Console.WriteLine("\nThe final error is " + finalError.ToString("F8"));

            Console.WriteLine("\nEnd Neural Network Back-Propagation demo\n");
            Console.ReadLine();
              }
              catch (Exception ex)
              {
            Console.WriteLine("Fatal: " + ex.Message);
            Console.ReadLine();
              }
        }
Ejemplo n.º 2
0
        static void Main(string[] args)
        {
            try
            {
                Console.WriteLine("\nBegin Neural Network training using Back-Propagation demo\n");

                Random rnd = new Random(1);                 // for random weights. not used.

                double[] xValues = new double[3] {
                    1.0, -2.0, 3.0
                };                                // inputs
                double[] yValues;                 // outputs
                double[] tValues = new double[2] {
                    0.1234, 0.8766
                };                                                                   // target values

                Console.WriteLine("The fixed input xValues are:");
                Helpers.ShowVector(xValues, 1, 8, true);

                Console.WriteLine("The fixed target tValues are:");
                Helpers.ShowVector(tValues, 4, 8, true);

                int numInput   = 3;
                int numHidden  = 4;
                int numOutput  = 2;
                int numWeights = (numInput * numHidden) + (numHidden * numOutput) + (numHidden + numOutput);

                Console.WriteLine("Creating a " + numInput + "-input, " + numHidden + "-hidden, " + numOutput + "-output neural network");
                Console.WriteLine("Using hard-coded tanh function for hidden layer activation");
                Console.WriteLine("Using hard-coded log-sigmoid function for output layer activation");

                BackPropNeuralNet bnn = new BackPropNeuralNet(numInput, numHidden, numOutput);

                //Console.WriteLine("\nGenerating random initial weights and bias values");
                //double[] initWeights = new double[numWeights];
                //for (int i = 0; i < initWeights.Length; ++i)
                //  initWeights[i] = (0.1 - 0.01) * rnd.NextDouble() + 0.01;

                Console.WriteLine("\nCreating arbitrary initial weights and bias values");
                double[] initWeights = new double[26] {
                    0.001, 0.002, 0.003, 0.004,
                    0.005, 0.006, 0.007, 0.008,
                    0.009, 0.010, 0.011, 0.012,

                    0.013, 0.014, 0.015, 0.016,

                    0.017, 0.018,
                    0.019, 0.020,
                    0.021, 0.022,
                    0.023, 0.024,

                    0.025, 0.026
                };

                Console.WriteLine("\nInitial weights and biases are:");
                Helpers.ShowVector(initWeights, 3, 8, true);

                Console.WriteLine("Loading neural network initial weights and biases into neural network");
                bnn.SetWeights(initWeights);

                double learnRate = 0.5;                // learning rate - controls the maginitude of the increase in the change in weights.
                double momentum  = 0.1;                // momentum - to discourage oscillation.
                Console.WriteLine("Setting learning rate = " + learnRate.ToString("F2") + " and momentum = " + momentum.ToString("F2"));

                int    maxEpochs   = 10000;
                double errorThresh = 0.00001;
                Console.WriteLine("\nSetting max epochs = " + maxEpochs + " and error threshold = " + errorThresh.ToString("F6"));

                int    epoch = 0;
                double error = double.MaxValue;
                Console.WriteLine("\nBeginning training using back-propagation\n");

                while (epoch < maxEpochs)                 // train
                {
                    if (epoch % 20 == 0)
                    {
                        Console.WriteLine("epoch = " + epoch);
                    }

                    yValues = bnn.ComputeOutputs(xValues);
                    error   = Helpers.Error(tValues, yValues);
                    if (error < errorThresh)
                    {
                        Console.WriteLine("Found weights and bias values that meet the error criterion at epoch " + epoch);
                        break;
                    }
                    bnn.UpdateWeights(tValues, learnRate, learnRate);
                    ++epoch;
                }                 // train loop

                double[] finalWeights = bnn.GetWeights();
                Console.WriteLine("");
                Console.WriteLine("Final neural network weights and bias values are:");
                Helpers.ShowVector(finalWeights, 5, 8, true);

                yValues = bnn.ComputeOutputs(xValues);
                Console.WriteLine("\nThe yValues using final weights are:");
                Helpers.ShowVector(yValues, 8, 8, true);

                double finalError = Helpers.Error(tValues, yValues);
                Console.WriteLine("\nThe final error is " + finalError.ToString("F8"));

                Console.WriteLine("\nEnd Neural Network Back-Propagation demo\n");
                Console.ReadLine();
            }
            catch (Exception ex)
            {
                Console.WriteLine("Fatal: " + ex.Message);
                Console.ReadLine();
            }
        } // Main