public static void LogicGatesExample()
{
//First layer is input layer
//Initialize a 2-layer ANN
//Two inputs, one output
int[] layers = new int[] { 2, 3, 1 };
FeedforwardNeuralNetwork fnn = new FeedforwardNeuralNetwork(layers, 1.0F, 0.1F);
//Train this many cycles
int numTrainingEpochs = 10000;
TrainingExample ex1 = new TrainingExample(new Matrix(new float[,]{ { 0 },
{ 0 } }),
new Matrix(new float[,]{ { 0 } }));
TrainingExample ex2 = new TrainingExample(new Matrix(new float[,]{ { 0 },
{ 1 } }),
new Matrix(new float[,]{ { 1 } }));
TrainingExample ex3 = new TrainingExample(new Matrix(new float[,]{ { 1 },
{ 0 } }),
new Matrix(new float[,]{ { 1 } }));
TrainingExample ex4 = new TrainingExample(new Matrix(new float[,]{ { 1 },
{ 1 } }),
new Matrix(new float[,]{ { 0 } }));
TrainingExample[] trainingExamples = { ex1, ex2, ex3, ex4 };
for (int i = 0; i < numTrainingEpochs; ++i) {
//Sets input matrices and output matrices and trains the network accordingly for all combinations
Random rand = new Random();
trainingExamples = trainingExamples.OrderBy(x => rand.Next()).ToArray();
for (int j = 0; j < 4; ++j) {
fnn.TrainIteration(trainingExamples[j].input, trainingExamples[j].expectedOutput, 1 - (1.0F * 0.1F / 4));
}
}
//After training, evaluates the network with respect to all possible inputs
//Prints their outputs to the console to see the result of training
float[,] inputArray = new float[,] { { 0 },
{ 0 } };
Matrix input = new Matrix(inputArray);
Matrix output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
inputArray = new float[,] { { 0 },
{ 1 } };
input = new Matrix(inputArray);
output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
inputArray = new float[,] { { 1 },
{ 0 } };
input = new Matrix(inputArray);
output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
inputArray = new float[,] { { 1 },
{ 1 } };
input = new Matrix(inputArray);
output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
}
public static void LogicGatesExample()
{
//First layer is input layer
//Initialize a 2-layer ANN
//Two inputs, one output
int[] layers = new int[] { 2, 3, 1 };
FeedforwardNeuralNetwork fnn = new FeedforwardNeuralNetwork(layers, 1.0F, 0.1F);
//Train this many cycles
int numTrainingEpochs = 10000;
TrainingExample ex1 = new TrainingExample(new Matrix(new float[, ] {
{ 0 },
{ 0 }
}),
new Matrix(new float[, ] {
{ 0 }
}));
TrainingExample ex2 = new TrainingExample(new Matrix(new float[, ] {
{ 0 },
{ 1 }
}),
new Matrix(new float[, ] {
{ 1 }
}));
TrainingExample ex3 = new TrainingExample(new Matrix(new float[, ] {
{ 1 },
{ 0 }
}),
new Matrix(new float[, ] {
{ 1 }
}));
TrainingExample ex4 = new TrainingExample(new Matrix(new float[, ] {
{ 1 },
{ 1 }
}),
new Matrix(new float[, ] {
{ 0 }
}));
TrainingExample[] trainingExamples = { ex1, ex2, ex3, ex4 };
for (int i = 0; i < numTrainingEpochs; ++i)
{
//Sets input matrices and output matrices and trains the network accordingly for all combinations
Random rand = new Random();
trainingExamples = trainingExamples.OrderBy(x => rand.Next()).ToArray();
for (int j = 0; j < 4; ++j)
{
fnn.TrainIteration(trainingExamples[j].input, trainingExamples[j].expectedOutput, 1 - (1.0F * 0.1F / 4));
}
}
//After training, evaluates the network with respect to all possible inputs
//Prints their outputs to the console to see the result of training
float[,] inputArray = new float[, ] {
{ 0 },
{ 0 }
};
Matrix input = new Matrix(inputArray);
Matrix output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
inputArray = new float[, ] {
{ 0 },
{ 1 }
};
input = new Matrix(inputArray);
output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
inputArray = new float[, ] {
{ 1 },
{ 0 }
};
input = new Matrix(inputArray);
output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
inputArray = new float[, ] {
{ 1 },
{ 1 }
};
input = new Matrix(inputArray);
output = fnn.Evaluate(input);
Console.WriteLine(output[1, 1]);
}
