static void TestNN(MultiLayerNetwork nw, List <GradeDigest> testDigests, int run)
{
List <Tuple <bool, double> > results = new List <Tuple <bool, double> >();
foreach (var gd in testDigests)
{
double[] output = new double[4];
nw.NetOUT(NNUtils.ToNetworkInput(GradeDigest.UnpackBits(gd.data)), out output);
int ans = inputCodes[NNUtils.Answer(output)];
double certainity = NNUtils.AnswerConfidence(output);
results.Add(new Tuple <bool, double>(ans == gd.grade, certainity));
}
double confidenceThreshold = results.Select(t => t.Item2).OrderBy(x => x).ElementAt((int)Math.Floor(results.Count * 0.95));
confidenceThreshold = 0.0001;
int testSuccess = 0;
int testFailure = 0;
int sureTestSuccess = 0;
int sureTestFailure = 0;
int unsure = 0;
foreach (var res in results)
{
if (res.Item1)
{
testSuccess++;
}
else
{
testFailure++;
}
if (res.Item2 > confidenceThreshold)
{
unsure++;
}
else if (res.Item1)
{
sureTestSuccess++;
}
else
{
sureTestFailure++;
}
}
Func <int, double> perc = x => ((double)x / testDigests.Count * 100);
Console.WriteLine("Test results (r/w%): {0:F2}/{1:F2}", perc(testSuccess), perc(testFailure));
Console.WriteLine("Test results (r/u/w%): {0:F2}/{1:F2}/{2:F2} (confidence threshold = {3})",
perc(sureTestSuccess), perc(unsure), perc(sureTestFailure), confidenceThreshold);
nw.SaveNW(String.Format("e:/Pronko/prj/Grader/ocr-data/grade-recognition_{0}_{1:F2}_{2:F2}.nn",
run, perc(testSuccess), perc(sureTestFailure)));
}
public static RecognitionResult RecognizeGrade(GradeDigest digest)
{
List <int> gradeCodes = new List <int> {
2, 3, 4, 5
};
double[] output = new double[4];
gradeRecognitionNetwork.NetOUT(NNUtils.ToNetworkInput(GradeDigest.UnpackBits(digest.data)), out output);
return(new RecognitionResult(
grade: gradeCodes[NNUtils.Answer(output)],
confident: NNUtils.AnswerConfidence(output) < recognitionConfidenceThreshold
));
}