public void PerformEpochAndAssert()
{
var trainingData = TrainingDataSet.For(inputs, outputs);
var errorRate = trainingMethod.PerformSingleEpochProducingErrorRate(trainingData);
AssertResultOfTrainingEpoch(errorRate);
}
public BackPropagationPerformanceComparisonContainer()
{
var neuralNetworkUnderTest =
NeuralNetwork
.For(NeuralNetworkContext.MaximumPrecision)
.WithInputLayer(neuronCount: 5, activationType: ActivationType.Sigmoid)
.WithHiddenLayer(neuronCount: 100, activationType: ActivationType.Sigmoid)
.WithHiddenLayer(neuronCount: 70, activationType: ActivationType.TanH)
.WithHiddenLayer(neuronCount: 40, activationType: ActivationType.TanH)
.WithHiddenLayer(neuronCount: 100, activationType: ActivationType.Sigmoid)
.WithOutputLayer(neuronCount: 2, activationType: ActivationType.Sigmoid)
.Build();
trainingData = new[] {
TrainingDataSet.For(new [] { 0.78, 0.99, 0.67, 0.72, 0.22 }, new [] { 0.12, 0.14 })
};
multiThreadedController = TrainingController.For(
BackPropagation
.WithConfiguration(neuralNetworkUnderTest, ParallelOptionsExtensions.UnrestrictedMultiThreadedOptions));
singleThreadedController = TrainingController.For(
BackPropagation
.WithConfiguration(neuralNetworkUnderTest, ParallelOptionsExtensions.SingleThreadedOptions));
}
public static TrainingDataSet TrainingDataFromRow(IrisDataRow row)
{
var species = Enumerable.Repeat(0.0, 3).ToArray();
species[Convert.ToInt32(row.Species) - 1] = 1.0;
return(TrainingDataSet.For(
row.PredictionDataPoints,
species.ToArray()));
}
public static TrainingDataSet ToTrainingData(this BankTransaction bankTransaction)
{
var inputs = bankTransaction.ToNetworkInputData();
var outputs = Enumerable.Repeat(0.0, 2).ToList();
outputs[Convert.ToInt32(bankTransaction.Class)] = 1;
return(TrainingDataSet.For(inputs.ToArray(), outputs.ToArray()));
}
private static List <TrainingDataSet> GetMyTrainingData()
{
// 2 tableaux
// inputs : double[22][64]
// outputs : double[22][3]
string filename = BASEFOLDER + "Base.txt";
string[] lines = File.ReadAllLines(filename); // lines va contenir 44 lines
int nbSamples = lines.Length / 2; // 22 exemples
double[][] inputs = new double[nbSamples][];
double[][] outputs = new double[nbSamples][];
for (int i = 0; i < nbSamples; i++)
{
inputs[i] = new double[64]; // inputs[i] est de type double[]
outputs[i] = new double[3];
// lines[2*i+0] contient la string "0000010101001001001010101010"
// lines[2*i+1] contient la string "C"
int k = 0;
foreach (char c in lines[2 * i])
{
if (c == '0')
{
inputs[i][k] = 0.0;
}
if (c == '1')
{
inputs[i][k] = 1.0;
}
k++;
}
if (lines[2 * i + 1] == "A")
{
outputs[i][0] = 1.0; outputs[i][1] = 0.0; outputs[i][2] = 0.0;
}
if (lines[2 * i + 1] == "B")
{
outputs[i][0] = 0.0; outputs[i][1] = 1.0; outputs[i][2] = 0.0;
}
if (lines[2 * i + 1] == "C")
{
outputs[i][0] = 0.0; outputs[i][1] = 0.0; outputs[i][2] = 1.0;
}
}
return(inputs.Select((input, i) => TrainingDataSet.For(input, outputs[i])).ToList());
}
private static List <TrainingDataSet> GetXorTrainingData()
{
var inputs = new[]
{
new[] { 0.0, 0.0 }, new[] { 0.0, 1.0 }, new[] { 1.0, 0.0 }, new[] { 1.0, 1.0 }
};
var outputs = new[]
{
new[] { 0.0 }, new[] { 1.0 }, new[] { 1.0 }, new[] { 0.0 }
};
return(inputs.Select((input, i) => TrainingDataSet.For(input, outputs[i])).ToList());
}
public static IEnumerable <TrainingDataSet> BuildTrainingDataForAllInputs(
IEnumerable <double[]> inputs,
IEnumerable <double[]> outputs)
{
return(inputs.Zip(outputs, (input, output) => TrainingDataSet.For(input, output)));
}
