public void ClassificationNeuralNetModel_PredictProbability_Single()
{
var numberOfObservations = 500;
var numberOfFeatures = 5;
var numberOfClasses = 5;
var random = new Random(32);
var observations = new F64Matrix(numberOfObservations, numberOfFeatures);
observations.Map(() => random.NextDouble());
var targets = Enumerable.Range(0, numberOfObservations)
.Select(i => (double)random.Next(0, numberOfClasses)).ToArray();
var sut = ClassificationNeuralNetModel.Load(() => new StringReader(m_classificationNeuralNetModelText));
var predictions = new ProbabilityPrediction[numberOfObservations];
for (int i = 0; i < numberOfObservations; i++)
{
predictions[i] = sut.PredictProbability(observations.Row(i));
}
var evaluator = new TotalErrorClassificationMetric <double>();
var actual = evaluator.Error(targets, predictions.Select(p => p.Prediction).ToArray());
Assert.AreEqual(0.762, actual);
}
public void ClassificationNeuralNetModel_Save()
{
var numberOfObservations = 500;
var numberOfFeatures = 5;
var numberOfClasses = 5;
var random = new Random(32);
var observations = new F64Matrix(numberOfObservations, numberOfFeatures);
observations.Map(() => random.NextDouble());
var targets = Enumerable.Range(0, numberOfObservations).Select(i => (double)random.Next(0, numberOfClasses)).ToArray();
var net = new NeuralNet();
net.Add(new InputLayer(numberOfFeatures));
net.Add(new DenseLayer(10));
net.Add(new SvmLayer(numberOfClasses));
var learner = new ClassificationNeuralNetLearner(net, new AccuracyLoss());
var sut = learner.Learn(observations, targets);
// save model.
var writer = new StringWriter();
sut.Save(() => writer);
// load model and assert prediction results.
sut = ClassificationNeuralNetModel.Load(() => new StringReader(writer.ToString()));
var predictions = sut.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.762, actual, 0.0000001);
}
/// <summary>
/// Method reads trained model from pravate field and load it to ClassificationNeuralNetModel object.
/// </summary>
private void LoadAnn()
{
if (!isAnnLoaded)
{
try
{
annModel = ClassificationNeuralNetModel.Load(() => new StringReader(allAnnModel));
isAnnLoaded = true;
}
catch (Exception ex)
{
isAnnLoaded = false;
throw new Exception(ex.Message);
}
}
}
public void ClassificationNeuralNetModel_Predict_Multiple()
{
var numberOfObservations = 500;
var numberOfFeatures = 5;
var numberOfClasses = 5;
var random = new Random(32);
var observations = new F64Matrix(numberOfObservations, numberOfFeatures);
observations.Map(() => random.NextDouble());
var targets = Enumerable.Range(0, numberOfObservations).Select(i => (double)random.Next(0, numberOfClasses)).ToArray();
var sut = ClassificationNeuralNetModel.Load(() => new StringReader(ClassificationNeuralNetModelText));
var predictions = sut.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.762, actual);
}
private DecodedLetters DecodeExtractedLetters(IEnumerable <IEnumerable <CsvRow> > lettersCollections, QrReaderData qrData)
{
string currentResult = null;
var isChecksumCorrect = false;
foreach (var letters in lettersCollections)
{
var csvReader = DataOutput.GetCsvParser(letters);
var targetName = "class";
var featureNames = csvReader.EnumerateRows(c => c != targetName).First().ColumnNameToIndex.Keys.ToArray();
var testObservations = csvReader.EnumerateRows(featureNames).ToF64Matrix();
testObservations.Map(p => p / 255);
var model = ClassificationNeuralNetModel.Load(() => new StreamReader("network.xml"));
var predictions = model.Predict(testObservations);
var stringBuilder = new StringBuilder();
foreach (var prediction in predictions)
{
stringBuilder.Append(Math.Abs(prediction - -1) < 0.01 ? '-' : Alphabet.Base32Alphabet.ToString()[(int)prediction]);
}
currentResult = stringBuilder.ToString();
isChecksumCorrect = Sha1Helper.IsChecksumCorrect(currentResult, qrData.MetaData.Checksum);
if (isChecksumCorrect)
{
break;
}
}
return(new DecodedLetters {
Letters = currentResult, IsChecksumOk = isChecksumCorrect
});
}