public void LoadModel(string path)
{
path = Path.GetDirectoryName(path) + "/" + Path.GetFileNameWithoutExtension(path) + ".dnn";
if (!File.Exists(path))
{
throw new FileNotFoundException($"Model file not found for prediction: {path}");
}
if (path.Equals(LastModelLoaded))
{
return;
}
try
{
Model = CNTKHelper.LoadModel(path);
var info = Path.GetDirectoryName(path) + "/" + Path.GetFileNameWithoutExtension(path) + ".txt";
List <string> lines = new List <string>();
using (StreamReader sr = new StreamReader(info))
{
while (sr.Peek() >= 0)
{
lines.Add(sr.ReadLine());
}
}
var shape = lines[2].Replace(" ", "").Replace("(", "").Replace(")", "").Split(',');
Predict(new DenseMatrix(Int32.Parse(shape[1]), Int32.Parse(shape[2])));
LastModelLoaded = path;
}
catch (Exception ex)
{
Console.WriteLine("Error: {0}\nCallStack: {1}\n Inner Exception: {2}", ex.Message, ex.StackTrace, ex.InnerException != null ? ex.InnerException.Message : "No Inner Exception");
throw ex;
}
}
public Matrix <double> Predict(Matrix <double> inputs)
{
if (Model == null)
{
return(new DenseMatrix(0, 0));
}
CNTK.Variable inputVar = Model.Arguments.Single();
double[] original = inputs.ToRowMajorArray();
//for (int i = 0; i < original.Length; i++)
//{
// if (i != 0 && i % 28 == 0)
// Console.Write("\n");
// Console.Write((int)(original[i] * 10) + ",");
//}
List <float> converted = original.Select(o => (float)o).ToList();
NDShape inputShape = inputVar.Shape;
var inputDataMap = new Dictionary <CNTK.Variable, Value>();
var inputVal = Value.CreateBatch(inputShape, converted, CNTKHelper.Device());
inputDataMap.Add(inputVar, inputVal);
CNTK.Variable outputVar = Model.Output;
// Create output data map. Using null as Value to indicate using system allocated memory.
// Alternatively, create a Value object and add it to the data map.
var outputDataMap = new Dictionary <CNTK.Variable, Value>();
outputDataMap.Add(outputVar, null);
// Start evaluation on the device
Model.Evaluate(inputDataMap, outputDataMap, CNTKHelper.Device());
// Get evaluate result as dense output
var outputVal = outputDataMap[outputVar];
var outputData = outputVal.GetDenseData <float>(outputVar);
double[,] arr = new double[outputData.Count, outputData[0].Count];
for (int i = 0; i < outputData.Count; i++)
{
for (int j = 0; j < outputData[i].Count; j++)
{
arr[i, j] = outputData[i][j];
}
}
return(DenseMatrix.OfArray(arr));
}
