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(); }
static void RunExamples() { 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.Run(mlConfigFile2, DeviceDescriptor.UseDefaultDevice(), token2, trainingProgress, null); MachineLearning.EvaluateModel(mlConfigFile2, result.BestModelFile, DeviceDescriptor.UseDefaultDevice()); //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($"****Bezier Curve Machine Learning Demonstration****"); Console.WriteLine(Environment.NewLine); var token = new CancellationToken(); MachineLearning.Run(mlConfigFile, DeviceDescriptor.UseDefaultDevice(), token, trainingProgress, null); //1. daily sales //modified dataset from preidct future sales var ds_mlConfigFile = "./model_mlconfigs/daily_sales.mlconfig"; Console.WriteLine(Environment.NewLine); Console.WriteLine($"****Predict Daily Sales for 10 items****"); Console.WriteLine(Environment.NewLine); MachineLearning.Run(ds_mlConfigFile, DeviceDescriptor.UseDefaultDevice(), new CancellationToken(), trainingProgress, 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($"****Predict Solar production****"); Console.WriteLine(Environment.NewLine); var token11 = new CancellationToken(); MachineLearning.Run(mlConfigFile11, DeviceDescriptor.UseDefaultDevice(), token11, trainingProgress, null); //2. Predict future sales,- Multiple Input variables //Kaggle competition dataset var mlConfigFile1 = "C:\\Users\\bhrnjica\\OneDrive - BHRNJICA\\AI Projects\\ann-custom-models" + "\\predict_future_sales.mlconfig"; Console.WriteLine(Environment.NewLine); Console.WriteLine($"****Predict Future Sales****"); Console.WriteLine(Environment.NewLine); var token1 = new CancellationToken(); MachineLearning.Run(mlConfigFile1, DeviceDescriptor.UseDefaultDevice(), token1, trainingProgress, 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.Run(mlConfigFile2, DeviceDescriptor.UseDefaultDevice(), token2, trainingProgress, model); }