static void Main(string[] args) { cntkModelToGraphviz(); return; //Iris flower recognition //Famous multi class classification datset: https://archive.ics.uci.edu/ml/datasets/iris string root = "C:\\sc\\github\\anndotnet\\src\\tool\\"; var mlConfigFile2 = $"{root}\\anndotnet.tool\\model_mlconfigs\\iris.mlconfig"; Console.WriteLine(Environment.NewLine); Console.WriteLine($"****Iris flower recognition****"); Console.WriteLine(Environment.NewLine); var token2 = new CancellationToken(); var result = MachineLearning.Run(mlConfigFile2, DeviceDescriptor.UseDefaultDevice(), token2, trainingProgress, null); ////evaluate model and export the result of testing MachineLearning.EvaluateModel(mlConfigFile2, result.BestModelFile, DeviceDescriptor.UseDefaultDevice()); //******run all configurations in the solution****** //for (int i = 0; i < 10; i++) // runAllml_configurations(strLocation1) //*****end of program***** Console.WriteLine("Press any key to continue!"); Console.ReadKey(); }
private static void runConvNetExample() { //cat&dag dataset location C:\sc\Datasets\cat-dog\train //train mlconfig var mlConfigFile1 = $"model_mlconfigs\\convNet.mlconfig"; var result = MachineLearning.Train(mlConfigFile1, trainingProgress, new CancellationToken(), null); }
static void TrainExamples() { for (int i = 0; i < 10; i++) { //Iris flower recognition //Famous multi class classification datset: https://archive.ics.uci.edu/ml/datasets/iris var mlConfigFile2 = "./model_mlconfigs/iris.mlconfig"; Console.WriteLine(Environment.NewLine); Console.Title = $"****Iris flower recognition****"; Console.WriteLine($"****Iris flower recognition****"); Console.WriteLine(Environment.NewLine); var token2 = new CancellationToken(); var result = MachineLearning.Train(mlConfigFile2, trainingProgress, token2, null); //Bezier Curve Machine Learning Demonstration //dataset taken form Code Project Article: //https://www.codeproject.com/Articles/1256883/Bezier-Curve-Machine-Learning-Demonstration var mlConfigFile = "./model_mlconfigs/BCML.mlconfig"; Console.WriteLine(Environment.NewLine); Console.WriteLine($"****Bezier Curve Machine Learning Demonstration****"); Console.WriteLine(Environment.NewLine); var token = new CancellationToken(); MachineLearning.Train(mlConfigFile, trainingProgress, token, null); //1. daily sales //modified dataset from preidct future sales var ds_mlConfigFile = "./model_mlconfigs/daily_sales.mlconfig"; Console.WriteLine(Environment.NewLine); Console.WriteLine($"****Predict Daily Sales for 10 items****"); Console.WriteLine(Environment.NewLine); MachineLearning.Train(ds_mlConfigFile, trainingProgress, new CancellationToken(), null); //1. solar production //CNTK Tutorial 106B_ https://cntk.ai/pythondocs/CNTK_106B_LSTM_Timeseries_with_IOT_Data.html var mlConfigFile11 = "C:\\Users\\bhrnjica\\OneDrive - BHRNJICA\\AI Projects\\ann-custom-models" + "\\solar_production.mlconfig"; Console.WriteLine(Environment.NewLine); Console.WriteLine($"****Predict Solar production****"); Console.WriteLine(Environment.NewLine); var token11 = new CancellationToken(); MachineLearning.Train(mlConfigFile11, trainingProgress, token11, null); //2. Predict future sales,- Multiple Input variables //Kaggle competition dataset var mlConfigFile1 = "C:\\Users\\bhrnjica\\OneDrive - BHRNJICA\\AI Projects\\ann-custom-models" + "\\predict_future_sales.mlconfig"; Console.WriteLine(Environment.NewLine); Console.WriteLine($"****Predict Future Sales****"); Console.WriteLine(Environment.NewLine); var token1 = new CancellationToken(); //MachineLearning.Train(mlConfigFile1, trainingProgress, token1, CustomNNModels.CustomModelCallEntryPoint); } }
private static void runExample(string title, string mlConfigPath, CreateCustomModel model = null) { var mlConfigFile2 = mlConfigPath; Console.WriteLine(Environment.NewLine); Console.WriteLine($"****{title}****"); Console.WriteLine(Environment.NewLine); var token2 = new CancellationToken(); MachineLearning.Train(mlConfigFile2, trainingProgress, token2, model); }
static void Main(string[] args) { //graphConvNetExample(); //runConvNetExample(); //cntkModelToGraphviz(); //Console.ReadKey(); //return; var rnd = new Random(1); Color randomColor = Color.FromArgb(rnd.Next(256), rnd.Next(256), rnd.Next(256)); //var root = "C:\\sc\\github\\anndotnet\\src\\tool"; //Iris flower recognition //Famous multi class classification datset: https://archive.ics.uci.edu/ml/datasets/iris var mlConfigFile3 = $"model_mlconfigs\\iris.mlconfig"; //run example // var token2 = new CancellationToken(); //train mlconfig var result = MachineLearning.Train(mlConfigFile3, trainingProgress, new CancellationToken(), null); //once the mode is trained you can write performance analysis of the model MachineLearning.PrintPerformance(mlConfigFile3); //SHow training history showTrainingHistory(mlConfigFile3); //evaluate model and export the result of testing //MLExport.ExportToCSV(mlConfigFile2, DeviceDescriptor.UseDefaultDevice(),"./model_mlconfigs/iris_result.csv" ).Wait(); //******run all configurations in the solution****** //for (int i = 0; i < 10; i++) // runAllml_configurations(strLocation1); //*****end of program***** Console.WriteLine("Press any key to continue!"); Console.ReadKey(); }
static void Main(string[] args) { string root = "C:\\sc\\github\\anndotnet\\src\\tool\\"; //transformDailyLevelVeanaLake(); //return; //regression var mlConfigFile1 = $"{root}anndotnet.wnd\\Resources\\Concrete\\ConcreteSlumpProject\\FFNModel.mlconfig"; //binary classification var mlConfigFile2 = $"{root}anndotnet.wnd\\Resources\\Titanic\\TitanicProject\\DNNModel.mlconfig"; //Multi-class classification //Famous multi class classification datset: https://archive.ics.uci.edu/ml/datasets/iris var mlConfigFile3 = "./model_mlconfigs/iris.mlconfig"; //run example var token2 = new CancellationToken(); //train mlconfig var result = MachineLearning.Train(mlConfigFile3, trainingProgress, token2, null); //once the mode is trained you can write performance analysis of the model MachineLearning.PrintPerformance(mlConfigFile1); //evaluate model and export the result of testing MLExport.ExportToCSV(mlConfigFile2, DeviceDescriptor.UseDefaultDevice(), "./model_mlconfigs/iris_result.csv").Wait(); //******run all configurations in the solution****** //string strLocation1 = "C:\\sc\\github\\anndotnet\\src\\tool\\"; //for (int i = 0; i < 10; i++) // runAllml_configurations(strLocation1); //*****end of program***** Console.WriteLine("Press any key to continue!"); Console.ReadKey(); }
private static void showTrainingHistory(string mlConfigFile3) { var history = MachineLearning.ShowTrainingHistory(mlConfigFile3); var data = history.First().Value; var header = history.First().Key; var x = header.Split(new char[] { ';' }, StringSplitOptions.RemoveEmptyEntries); var model = DataProcessing.Core.ChartComponent.LinePlot("Model Evaluation", "Training Data", data.Select(d => (double)d.Item1).ToArray(), data.Select(d => (double)d.Item4).ToArray(), Color.Blue, MarkerType.Circle, "Iterations", x.Last()); var ss = DataProcessing.Core.ChartComponent.LineSeries("Validation Data", data.Select(d => (double)d.Item1).ToArray(), data.Select(d => (double)d.Item5).ToArray(), Color.Orange, MarkerType.Circle); model.Series.Add(ss); model.LegendPosition = LegendPosition.LeftTop; showPlot(model).Wait(); }