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 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);
}
// Main Functions
public List <int> MakeMove(List <int> GameState, int Player, bool Training = false)
{
// Get all vaild moves
List <double> GameBoard = GameState.Select <int, double>(i => i).ToList();
NormaliseData(GameBoard);
List <double> MoveList = ANN.Get_Network_Output(GameBoard);
// Remove impossible moves
for (int Move = 0; Move < MoveList.Count; Move++)
{
if (GameState[Move] != 0)
{
MoveList[Move] = 0;
}
}
// Select move
int SelectedMove;
if (Training == true)
{
//SelectedMove = MakeRandomMove(MoveList); // returns random move -> un directed search for optimal move
//SelectedMove = MoveList.IndexOf(MoveList.Max()); // Selects only best move -> limits seach for optimal moves
SelectedMove = SelectMoveBasedOnProbability(MoveList); // Employs sofmax function to choose move (only applies softmax to non 0 elements of MoveList). -> randomly selects move based on probility distrubution
// Save move and score to episodedata to retrain network later
List <double> ActionRow = new List <double> {
};
ActionRow.AddRange(GameBoard);
ActionRow.AddRange(MoveList);
ActionRow.Add((SelectedMove + 9));
EpisodeData.Add(ActionRow);
}
else
{
SelectedMove = MoveList.IndexOf(MoveList.Max()); // Selects only best move -> limits seach for optimal moves
}
// build resulting game board
List <int> NewGameBoard = new List <int> {
};
for (int Square = 0; Square < GameBoard.Count; Square++)
{
if (Square == SelectedMove)
{
NewGameBoard.Add(Player);
}
else
{
NewGameBoard.Add(Convert.ToInt32(GameState[Square]));
}
}
return(NewGameBoard);
}
public void GetNetworkOutput_CheckOutputCorrect()
{
// Arrange
ANeuralNetwork ANN = new ANeuralNetwork();
ANN.Load_Network("Test.net");
List <double> InputData = new List <double> {
0.1, 0.9
};
double DesiredOutput = 0.60761339;
// Act
List <double> Output = ANN.Get_Network_Output(InputData);
// Assert
Assert.AreEqual(DesiredOutput, Math.Round(Output[0], 8));
}
