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);
}