private BasicNetwork LoadNetwork(string netData, int input, int output, int h_c, int h_l) { byte[] byteData = Convert.FromBase64String(netData); double[] data = new double[byteData.Length / 8]; Buffer.BlockCopy(byteData, 0, data, 0, byteData.Length); BasicNetwork network = GenNetwork(input, output, h_c, h_l); network.DecodeFromArray(data); return(network); }
/// <summary> /// Update the swarm's best position /// </summary> protected void UpdateGlobalBestPosition() { bool bestUpdated = false; for (int i = 0; i < m_populationSize; i++) { if ((m_bestVectorIndex == -1) || IsScoreBetter(m_bestErrors[i], m_bestErrors[m_bestVectorIndex])) { m_bestVectorIndex = i; bestUpdated = true; } } if (bestUpdated) { m_va.Copy(m_bestVector, m_bestVectors[m_bestVectorIndex]); m_bestNetwork.DecodeFromArray(m_bestVector); Error = m_bestErrors[m_bestVectorIndex]; } }
private BasicNetwork LoadNetwork(Network network_data) { byte[] byteData = Convert.FromBase64String(network_data.data); double[] data = new double[byteData.Length / 8]; Buffer.BlockCopy(byteData, 0, data, 0, byteData.Length); BasicNetwork network = new BasicNetwork(); network = new BasicNetwork(); network.AddLayer(new BasicLayer(null, true, 30 * 2 * 2)); for (int i = 0; i < network_data.hidden_count; i++) { network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, network_data.hidden_length)); } network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, 1)); network.Structure.FinalizeStructure(); network.Reset(); network.DecodeFromArray(data); return(network); }
/// <summary> /// This is based off of this article: /// http://www.codeproject.com/Articles/54575/An-Introduction-to-Encog-Neural-Networks-for-C /// </summary> /// <remarks> /// Go here for documentation of encog: /// http://www.heatonresearch.com/wiki /// /// Download link: /// https://github.com/encog/encog-dotnet-core/releases /// </remarks> private void btnXOR_Click(object sender, RoutedEventArgs e) { try { _trainingData = null; _results = null; BasicNetwork network = new BasicNetwork(); #region Create nodes // Create the network's nodes //NOTE: Using ActivationSigmoid, because there are no negative values. If there were negative, use ActivationTANH //http://www.heatonresearch.com/wiki/Activation_Function //NOTE: ActivationSigmoid (0 to 1) and ActivationTANH (-1 to 1) are pure but slower. A cruder but faster function is ActivationElliott (0 to 1) and ActivationElliottSymmetric (-1 to 1) //http://www.heatonresearch.com/wiki/Elliott_Activation_Function network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 2)); // input layer network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 6)); // hidden layer network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 1)); // output layer network.Structure.FinalizeStructure(); // Randomize the links network.Reset(); #endregion #region Training data // Neural networks must be trained before they are of any use. To train this neural network, we must provide training // data. The training data is the truth table for the XOR operator. The XOR has the following inputs: double[][] xor_input = new[] { new[] { 0d, 0d }, new[] { 1d, 0d }, new[] { 0d, 1d }, new[] { 1d, 1d }, }; // And the expected outputs double[][] xor_ideal_output = new[] { new[] { 0d }, new[] { 1d }, new[] { 1d }, new[] { 0d }, }; _trainingData = GetDrawDataFromTrainingData(xor_input, xor_ideal_output); #endregion #region Train network INeuralDataSet trainingSet = new BasicNeuralDataSet(xor_input, xor_ideal_output); // This is a good general purpose training algorithm //http://www.heatonresearch.com/wiki/Training ITrain train = new ResilientPropagation(network, trainingSet); List <double> log = new List <double>(); int trainingIteration = 1; do { train.Iteration(); log.Add(train.Error); trainingIteration++; } while ((trainingIteration < 2000) && (train.Error > 0.001)); // Paste this into excel and chart it to see the error trend string logExcel = string.Join("\r\n", log); #endregion #region Test //NOTE: I initially ran a bunch of tests, but the network always returns exactly the same result when given the same inputs //var test = Enumerable.Range(0, 1000). // Select(o => new { In1 = _rand.Next(2), In2 = _rand.Next(2) }). var test = xor_input. Select(o => new { In1 = Convert.ToInt32(o[0]), In2 = Convert.ToInt32(o[1]) }). Select(o => new { o.In1, o.In2, Expected = XOR(o.In1, o.In2), NN = CallNN(network, o.In1, o.In2), }). Select(o => new { o.In1, o.In2, o.Expected, o.NN, Error = Math.Abs(o.Expected - o.NN) }). OrderByDescending(o => o.Error). ToArray(); #endregion #region Test intermediate values // It was only trained with inputs of 0 and 1. Let's see what it does with values in between var intermediates = Enumerable.Range(0, 1000). Select(o => new { In1 = _rand.NextDouble(), In2 = _rand.NextDouble() }). Select(o => new { o.In1, o.In2, NN = CallNN(network, o.In1, o.In2), }). OrderBy(o => o.In1). ThenBy(o => o.In2). //OrderBy(o => o.NN). ToArray(); #endregion #region Serialize/Deserialize // Serialize it string weightDump = network.DumpWeights(); double[] dumpArray = weightDump.Split(','). Select(o => double.Parse(o)). ToArray(); //TODO: Shoot through the layers, and store in some custom structure that can be serialized, then walked through to rebuild on deserialize //string[] layerDump = network.Structure.Layers. // Select(o => o.ToString()). // ToArray(); // Create a clone BasicNetwork clone = new BasicNetwork(); clone.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 2)); clone.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 6)); clone.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 1)); clone.Structure.FinalizeStructure(); clone.DecodeFromArray(dumpArray); // Test the clone string cloneDump = clone.DumpWeights(); bool isSame = weightDump == cloneDump; var cloneTests = xor_input. Select(o => new { Input = o, NN = CallNN(clone, o[0], o[1]), }).ToArray(); #endregion #region Store results double[] matchValues = new[] { 0d, 1d }; double matchRange = .03; //+- 5% of target value would be considered a match _results = intermediates. Select(o => Tuple.Create(new Point(o.In1, o.In2), o.NN, IsMatch(o.NN, matchValues, matchRange))). ToArray(); #endregion } catch (Exception ex) { MessageBox.Show(ex.ToString(), this.Title, MessageBoxButton.OK, MessageBoxImage.Error); } finally { RedrawResults(); } }