public void TrainNetwork(NetworkModel network, NetworksStorageModel storage, QueryDataFormat dataFormat, TaskProgressVM taskProgressVM = null)
{
if (taskProgressVM != null)
{
taskProgressVM.EndValue = TrainDatas.Count;
taskProgressVM.TaskName = "Training network";
}
if (network.Generation == 0)
{
SaveHistory(network);
}
Parallel.ForEach(TrainDatas, (model) =>
{
var trainData = new MNIST28x28TrainData(model);
network.Train(trainData);
if (taskProgressVM != null)
{
taskProgressVM.Value++;
}
});
network.Generation++;
SaveHistory(network);
_workshopModel.SaveStorageAsync(storage.Id.ToString());
}
public async void LoadTrainFile(string fileName, QueryDataFormat dataFormat, TaskProgressVM taskVM = null)
{
TrainDatas.Clear();
switch (dataFormat)
{
case QueryDataFormat.BlackMNIST28x28:
var mnistDatas = await _fileService.ReadFromFileAsync(fileName, new CsvMNISTFileReadStrategy());
if (taskVM != null)
{
taskVM.EndValue = mnistDatas.Count();
}
foreach (var data in mnistDatas)
{
var dataModel = new QueryDataModel();
dataModel.Marker = data.Marker.ToString();
dataModel.InputValues = (from v in data.PixelsValues select(float) v).ToArray();
TrainDatas.Add(dataModel);
if (taskVM != null)
{
taskVM.Value++;
}
}
break;
}
}
public string QueryNetwork(NetworkModel network, QueryDataModel queryData, QueryDataFormat dataFormat)
{
switch (dataFormat)
{
case QueryDataFormat.BlackMNIST28x28:
var outputs = network.Query(queryData.InputValues);
return(queryData.Marker = Array.IndexOf(outputs, outputs.Max()).ToString());
default:
return("Bad request");
}
}
public QueryDataModel BackQuery(NetworkModel network, QueryDataModel targetData, QueryDataFormat dataFormat)
{
switch (dataFormat)
{
case QueryDataFormat.BlackMNIST28x28:
var inputs = network.BackQuery(targetData.OutputValues);
var data = new QueryDataModel()
{
InputValues = inputs,
Marker = targetData.Marker,
OutputValues = targetData.OutputValues
};
var readyData = new MNIST28x28BackQueryData(data);
return(readyData);
default:
return(null);
}
}
