public void TrainMiniBatch_XORGateTest_SingleBatch()
{
// Arrange
ANeuralNetwork ANN = new ANeuralNetwork();
ANN.Create_Network(new List <int> {
2, 3, 1
});
List <List <double> > InputData = new List <List <double> > {
new List <double> {
1, 1
}, new List <double> {
0, 1
}, new List <double> {
1, 0
}, new List <double> {
0, 0
},
};
List <List <double> > OutputData = new List <List <double> > {
new List <double> {
0
}, new List <double> {
1
}, new List <double> {
1
}, new List <double> {
0
}
};
List <List <double> > ValidationData = new List <List <double> > {
new List <double> {
1, 1
}, new List <double> {
0, 1
}, new List <double> {
1, 0
}, new List <double> {
0, 0
},
};
List <double> ExpectedOutput = new List <double> {
0, 1, 1, 0
};
// Act
ANN.Train_MiniBatch(InputData, OutputData, InputData.Count, "XOR_SinleBatch.dat");
List <double> Results = new List <double> {
};
for (int i = 0; i < 4; i++)
{
List <double> result = ANN.Get_Network_Output(ValidationData[i]);
Results.Add(Math.Round(result[0], 0));
Console.Write("Output = " + Results[i] + " ");
}
// Assert
CollectionAssert.AreEqual(ExpectedOutput, Results);
}
public void TrainMiniBatch_ConvergenceTest_ValuesNOT01()
{
// Arrange
ANeuralNetwork ANN = new ANeuralNetwork();
ANN.Load_Network("Test.net");
List <List <double> > InputData = new List <List <double> > {
new List <double> {
10, 10
}
};
List <List <double> > OutputData = new List <List <double> > {
new List <double> {
10
}
};
// Act
ANN.Train_MiniBatch(InputData, OutputData, InputData.Count, "MiniBatch_Convergence.dat");
List <double> Result = ANN.Get_Network_Output(InputData[0]);
// Assert
Console.Write("Output = " + Result[0]);
Assert.AreNotEqual(OutputData[0][0], Math.Round(Result[0], 1));
}
public void UpdateNetwork()
{
List <List <double> > TrainingInputs = new List <List <double> > {
};
List <List <double> > TrainingOutput = new List <List <double> > {
};
for (int Row = 0; Row < EpisodeData.Count; Row++)
{
List <List <double> > SymertrisedRow = SymertriseData(EpisodeData[Row]);
for (int DataRow = 0; DataRow < SymertrisedRow.Count; DataRow++)
{
List <List <double> > Results = SplitInputsFromOutputs(SymertrisedRow[DataRow], 9);
TrainingInputs.Add(Results[0]);
TrainingOutput.Add(Results[1]);
}
}
ANN.Train_MiniBatch(TrainingInputs, TrainingOutput, TrainingInputs.Count, "QlearnTraining.txt", 0.001, 0.7, 1e6, 1e-8, false);
EpisodeData.Clear();
}