public void ShouldTrainForXor()
{
var brain = new NeuralNetwork(2, new int[3] {
5, 5, 5
}, 1, ActivationFunction.LogSigmoid);
var deepLearning = new DeepLearning(brain, 0.00001);
var inputs = new Matrix[4];
inputs[0] = new Matrix(2, 1);
inputs[0][0, 0] = 0;
inputs[0][1, 0] = 0;
inputs[1] = new Matrix(2, 1);
inputs[1][0, 0] = 1;
inputs[1][1, 0] = 0;
inputs[2] = new Matrix(2, 1);
inputs[2][0, 0] = 0;
inputs[2][1, 0] = 1;
inputs[3] = new Matrix(2, 1);
inputs[3][0, 0] = 1;
inputs[3][1, 0] = 1;
var targets = new Matrix[4];
targets[0] = new Matrix(1, 1);
targets[0][0, 0] = 0;
targets[1] = new Matrix(1, 1);
targets[1][0, 0] = 1;
targets[2] = new Matrix(1, 1);
targets[2][0, 0] = 1;
targets[3] = new Matrix(1, 1);
targets[3][0, 0] = 0;
var rand = new Random();
for (int i = 0; i < 10000; i++)
{
var t = rand.Next(4);
deepLearning.Train(inputs[t], targets[t]);
}
var i0 = brain.Prediction(inputs[0]); // epected 0
var i2 = brain.Prediction(inputs[1]); // expected 1
var i3 = brain.Prediction(inputs[2]); // expected 1
var i4 = brain.Prediction(inputs[3]); //expected 0
Assert.IsFalse(false);
}
static void Main(string[] args)
{
int nInput = 2;
int nOutput = 1;
int neuronsPerLayers = 10;
int nLayers = 3;
DeepLearning slp = new DeepLearning(nInput, neuronsPerLayers, nOutput, nLayers);
//SingleLayerPerceptron slp = new SingleLayerPerceptron(nInput, neuronsPerLayers, nOutput);
var nPoints = 10000;
for (int i = 0; i < nPoints; i++)
{
Random random = new Random(Guid.NewGuid().GetHashCode());
int value1 = random.Next() % 2;
random = new Random(Guid.NewGuid().GetHashCode());
int value2 = random.Next() % 2;
random = new Random(Guid.NewGuid().GetHashCode());
int value3 = random.Next() % 2;
//int xor = XOR(value1, value2);
int correctValue = ComplexeBoolMethod(value1, value2, value3);
slp.Train(new List <double>()
{
value1, value2
}, new List <double>()
{
correctValue
});
var percent = Math.Round(i * 100.0 / nPoints, 4);
//if (percent == (int)percent)
//{
// Console.Clear();
// Console.WriteLine(percent);
//}
}
double n = 0;
int nTestCase = 200;
for (int i = 0; i < nTestCase; i++)
{
Random random = new Random(Guid.NewGuid().GetHashCode());
int value1 = random.Next() % 2;
random = new Random(Guid.NewGuid().GetHashCode());
int value2 = random.Next() % 2;
random = new Random(Guid.NewGuid().GetHashCode());
int value3 = random.Next() % 2;
//int xor = XOR(value1, value2);
int correctValue = ComplexeBoolMethod(value1, value2, value3);
var guess = slp.Predict(new List <double>()
{
value1, value2
});
double result = guess[0] > 0.5 ? 1 : 0;
bool correct = result == correctValue;
Console.WriteLine("correct value : " + correctValue);
Console.WriteLine("guess : " + guess[0]);
Console.WriteLine("--");
if (correct)
{
n++;
}
}
var path = "C:\\Users\\Boudart Loic\\source\\repos\\MachineLearning\\MachineLearning\\brain.json";
slp.Save(path);
slp.Load(path);
//to debug
{
Random random = new Random(Guid.NewGuid().GetHashCode());
int value1 = random.Next() % 2;
random = new Random(Guid.NewGuid().GetHashCode());
int value2 = random.Next() % 2;
int xor = XOR(value1, value2);
slp.Train(new List <double>()
{
value1, value2
}, new List <double>()
{
xor
});
//if (percent == (int)percent)
//{
// Console.Clear();
// Console.WriteLine(percent);
//}
}
Console.WriteLine(n / nTestCase);
Console.Read();
}
