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 void Main(String[] args)
{
//var sourceDir = Network.Default.DatasetDirectory + "\\Base";
//var outputDir = Network.Default.DatasetDirectory;
//CharacterDatasetHandler.GenerateDatasetEntry(sourceDir, outputDir);
var trainAlphabet = new AlphabetDataset(EnumDatasetType.Train);
var validationAlphabet = new AlphabetDataset(EnumDatasetType.Validation);
var testAlphabet = new AlphabetDataset(EnumDatasetType.Test);
var cycles = DatasetUtils.BuildCyclesFromDataset(trainAlphabet, validationAlphabet, testAlphabet);
var network = new MultiLayerNetwork(cycles);
network.InitializeNetwork();
ObservableCollection <string> lettersReturned;
var likelyResults = "";
Console.WriteLine("\nThe Network has initialized. The test of the Network will start\n");
Console.ReadLine();
var numberOfExamples = 0;
var numberOfCorrectResults = 0;
for (int i = 0; i < network.Cycles.Count; i++)
{
//network.Cycles[i].ExamplesTest = network.RandomizeList(network.Cycles[i].ExamplesTest);
foreach (Example example in network.Cycles[i].ExamplesTest)
{
var outputFromTrainedNetwork = network.CheckElement(example);
lettersReturned = DatasetUtils.GetLettersFromOutputArray(outputFromTrainedNetwork);
likelyResults = string.Join(" ", lettersReturned);
Console.WriteLine("Cycle: " + (i + 1) + " Expected result: " + example.Name + ". Likely results: " + likelyResults);
numberOfExamples++;
if (lettersReturned.Contains(example.Name))
{
numberOfCorrectResults++;
}
likelyResults = "";
}
}
var result = ((float)numberOfCorrectResults / (float)numberOfExamples) * 100;
Console.WriteLine("Accuraty rate of Network: " + result + "%");
Console.ReadLine();
}
public static MultiLayerNetwork CreateTwoLayerPerceptronNetwork(int layer1Size, int layer2Size, int inputDim)
{
NetworkLayer layer1 = CreatePerceptronLayer(layer1Size, inputDim);
NetworkLayer layer2 = CreatePerceptronLayer(layer2Size, layer1Size);
IList <NetworkLayer> layers = new List <NetworkLayer>()
{
layer1, layer2
};
return(MultiLayerNetwork.Of(layers));
}
static void Main(string[] args)
{
MultiLayerNetwork nw = new MultiLayerNetwork(GradeDigest.dataSize, new int[] { 100, 4 });
List <GradeDigest> trainDigests = GradeDigestSet.staticInstance.GetDigestList();
List <GradeDigest> testDigests = GradeDigestSet.Read("e:/Pronko/prj/Grader/ocr-data/test-data/grade-digests.db").GetDigestList();
for (int trainingRun = 1; trainingRun <= 100; trainingRun++)
{
Util.Timed(String.Format("training run #{0}", trainingRun), () => {
int c = 0;
foreach (var gd in trainDigests.Shuffle())
{
double[] desiredOutput = new double[4];
desiredOutput[inputCodes.IndexOf(gd.grade)] = 1;
nw.LearnNW(NNUtils.ToNetworkInput(GradeDigest.UnpackBits(gd.data)), desiredOutput, 0.1);
c++;
}
});
TestNN(nw, testDigests, trainingRun);
}
}
private void InitializeTwoLayerNetwork()
{
twoLayerNetwork = NeuralNetworksUtils.CreateTwoLayerPerceptronNetwork(
Commons.FIRST_LAYER_DIM, Commons.SECOND_LAYER_DIM, Commons.NEURONS_DIM);
}