PrepareNNData(Dictionary <string, string> dicMParameters, CreateCustomModel customModel, DeviceDescriptor device)
{
try
{
//create factory object
MLFactory f = CreateMLFactory(dicMParameters);
//create learning params
var strLearning = dicMParameters["learning"];
LearningParameters lrData = MLFactory.CreateLearningParameters(strLearning);
//create training param
var strTraining = dicMParameters["training"];
TrainingParameters trData = MLFactory.CreateTrainingParameters(strTraining);
//set model component locations
var dicPath = MLFactory.GetMLConfigComponentPaths(dicMParameters["paths"]);
//
trData.ModelTempLocation = $"{dicMParameters["root"]}\\{dicPath["TempModels"]}";
trData.ModelFinalLocation = $"{dicMParameters["root"]}\\{dicPath["Models"]}";
var strTrainPath = $"{dicMParameters["root"]}\\{dicPath["Training"]}";
var strValidPath = (string.IsNullOrEmpty(dicPath["Validation"]) || dicPath["Validation"] == " ") ? "":
$"{dicMParameters["root"]}\\{dicPath["Validation"]}";
//data normalization in case the option is enabled
//check if network contains Normalization layer and assign value to normalization parameter
if (dicMParameters["network"].Contains("Normalization"))
{
trData.Normalization = new string[] { MLFactory.m_NumFeaturesGroupName }
}
;
//perform data normalization according to the normalization parameter
List <Variable> networkInput = NormalizeInputLayer(trData, f, strTrainPath, strValidPath, device);
//create network parameters
Function nnModel = CreateNetworkModel(dicMParameters["network"], networkInput, f.OutputVariables, customModel, device);
//create minibatch spurce
var mbs = new MinibatchSourceEx(trData.Type, f.StreamConfigurations.ToArray(), strTrainPath, strValidPath, MinibatchSource.InfinitelyRepeat, trData.RandomizeBatch);
//return ml parameters
return(f, lrData, trData, nnModel, mbs);
}
catch (Exception)
{
throw;
}
}
