public static Image <Rgba32> GenerateImage(List <List <Node> > network, Dictionary <string, bool> state, int size) { var img2 = new Image <Rgba32>(size, size); for (int x = 0; x < size; x++) { for (int y = 0; y < size; y++) { double outx = x / (double)size * 2.0 - 1.0; double outy = y / (double)size * 2.0 - 1.0; var input = MLHelper.ConstructInput(state, outx, outy); var lastNode = NetworkBuilder.ForwardProp(network, input); //byte r = (byte)(255 * (1 - lastNode)); //byte g = 0; //byte b = 0; //=MAX((1-((A2+1)/2) / 2 * 3) *255; 0) byte r = (byte)Math.Max((1 - ((lastNode + 1) / 2.0) / 2.0 * 3.0) * 255.0, 0); //=MAX((1-((A2+1)/2) / 2 * 6) *128; 0) byte g = (byte)Math.Max((1 - ((lastNode + 1) / 2.0) / 2.0 * 6.0) * 128.0, 0); //=MIN(MAX((((A2+1 - 0,6666)/2 / 2 *3) ) *255; 0); 255) byte b = (byte)Math.Min(Math.Max((((lastNode + 1 - (2.0 / 3.0)) / 2.0 / 2.0 * 3.0)) * 255.0, 0), 255); img2[x, y] = new Rgba32(r, g, b); //var pntX = ((pnt.X + 1.0) / 2.0) * size; //var pntY = ((pnt.Y + 1.0) / 2.0) * size; } } return(img2); }
public static double GetLoss(List <List <Node> > network, Dictionary <string, bool> state, List <Puntje> dataPoints) { double loss = 0; for (var i = 0; i < dataPoints.Count; i++) { var dataPoint = dataPoints[i]; var input = MLHelper.ConstructInput(state, dataPoint.X, dataPoint.Y); var output = NetworkBuilder.ForwardProp(network, input); loss += Errors.SQUARE.Error(output, dataPoint.Label); } return(loss / dataPoints.Count); }
private void Go() { var lijstje = DataSetGenerator.Generate(); int size = 500; var img = new Image <Rgba32>(size, size); MLImager.AddPointsToImage(img, lijstje); using (var fs = new FileStream("output.png", FileMode.Create, FileAccess.Write, FileShare.Read)) { img.SaveAsPng(fs); } lijstje.Shuffle(); var testData = lijstje.Take(lijstje.Count / 2).ToList(); var trainData = lijstje.Skip(lijstje.Count / 2).ToList(); foreach (var inp in INPUTS.INPUTSDICT) { state[inp.Key] = false; } state["x"] = true; state["y"] = true; //state["xTimesY"] = true; //var networkShape = new List<int>() { 2, 1, 1 }; var networkShape = new List <int>() { 2, 8, 8, 8, 8, 8, 8, 1 }; var inputIds = new List <string>() { "x", "y" }; var network = NetworkBuilder.BuildNetwork(networkShape, Activations.TANH, Activations.TANH, null, inputIds, false); var w = Stopwatch.StartNew(); for (int i = 0; i < 100000; i++) { foreach (var trainPoint in trainData) { var input = MLHelper.ConstructInput(state, trainPoint.X, trainPoint.Y); NetworkBuilder.ForwardProp(network, input); NetworkBuilder.BackProp(network, trainPoint.Label, Errors.SQUARE); if ((i + 1) % batchSize == 0) { NetworkBuilder.UpdateWeights(network, learningrate, regularizationRate); } } lossTrain = MLHelper.GetLoss(network, state, trainData); lossTest = MLHelper.GetLoss(network, state, testData); Console.WriteLine($"{i}: LossTrain: {lossTrain} LossTest: {lossTest}"); if (w.Elapsed.TotalSeconds > 1) { var img2 = MLImager.GenerateImage(network, state, size); MLImager.AddPointsToImage(img2, lijstje); try { using (var fs = new FileStream("output2.png", FileMode.Create, FileAccess.Write, FileShare.Read)) { img2.SaveAsPng(fs); } } catch (Exception ex) { } w.Restart(); } //for (int y = 1; y < network.Count; y++) //{ // var layer = network[y]; // Console.WriteLine(y); // foreach (var node in layer) // { // var sb = new StringBuilder(); // foreach (var link in node.InputLinks) // { // sb.Append($"Lnk: {link.Weight} "); // } // Console.WriteLine($" {sb.ToString()}"); // } // Console.WriteLine(); //} //double cccc = 0; //foreach (var testPoint in testData) //{ // var input = ConstructInput(testPoint.X, testPoint.Y); // var result = NetworkBuilder.ForwardProp(network, input); // var res = Math.Abs(result - testPoint.Label); // cccc += res; //} //Console.WriteLine($"Res: {cccc}"); } }