static void Main(string[] args)
{
NeuralNetwork net2 = new NeuralNetwork(new int[] { 2, 3, 1 }, new TanhActivationFunction());
net2.FeedForward(new double[] { 0, 0 });
Console.WriteLine("Output for input 0, 0: " + net2.layers[net2.layers.Length - 1].activations[0]);
net2.FeedForward(new double[] { 0, 1 });
Console.WriteLine("Output for input 0, 1: " + net2.layers[net2.layers.Length - 1].activations[0]);
net2.FeedForward(new double[] { 1, 0 });
Console.WriteLine("Output for input 1, 0: " + net2.layers[net2.layers.Length - 1].activations[0]);
net2.FeedForward(new double[] { 1, 1 });
Console.WriteLine("Output for input 1, 1: " + net2.layers[net2.layers.Length - 1].activations[0]);
net2.Fit(new double[, ]
{
{ 0, 0 },
{ 0, 1 },
{ 1, 0 },
{ 1, 1 }
}, new double[, ]
{
{ 0 },
{ 1 },
{ 1 },
{ 0 }
}, 60001, 1);
net2.FeedForward(new double[] { 0, 0 });
Console.WriteLine("Output for input 0, 0: " + net2.layers[net2.layers.Length - 1].activations[0]);
net2.FeedForward(new double[] { 0, 1 });
Console.WriteLine("Output for input 0, 1: " + net2.layers[net2.layers.Length - 1].activations[0]);
net2.FeedForward(new double[] { 1, 0 });
Console.WriteLine("Output for input 1, 0: " + net2.layers[net2.layers.Length - 1].activations[0]);
net2.FeedForward(new double[] { 1, 1 });
Console.WriteLine("Output for input 1, 1: " + net2.layers[net2.layers.Length - 1].activations[0]);
Console.ReadKey();
}
static void Main(string[] args)
{
NeuralNetwork NN = new NeuralNetwork(new int[] { 4, 7, 3 }, LearningRate: 0.05, Momentum: 0.001, Decay: 0.0001);
double[][] entries = getInputs(true);
double[][] learningData, expectedOutputData, testingData, testingDataOutputData;
int testingDataElementsCount = (int)(entries.Length * 0.20);
Console.WriteLine("Shuffle data and create testing set");
Shuffle(entries, testingDataElementsCount, out learningData, out expectedOutputData, out testingData, out testingDataOutputData);
Console.WriteLine("Training");
NN.Train(learningData, expectedOutputData, MaxEpoches: 2000, MSE: 0.2f);
Console.WriteLine("Training finished");
Console.WriteLine("Processing testing cases");
int rightAnswers = 0;
NeuronLayer result;
for (int i = 0; i < testingData.Length; i++)
{
double[] entry = testingData[i];
result = NN.FeedForward(new double[] { entry[0], entry[1], entry[2], entry[3] }.GetEnumerator());
double maxVal = double.MinValue;
int maxValIndex = 0;
int rightAnswerIndex = 0;
for (int j = 0; j < result.Capacity; j++)
{
if (result[j].activation > maxVal)
{
maxVal = result[j].activation;
maxValIndex = j;
}
if (testingDataOutputData[i][j] == 1)
{
rightAnswerIndex = j;
}
}
if (maxValIndex == rightAnswerIndex)
{
rightAnswers++;
}
else
{
for (int j = 0; j < result.Capacity; j++)
{
Console.Write(String.Format("{0} = {1};", Math.Round(result[j].activation, 2), testingDataOutputData[i][j]));
}
Console.WriteLine();
}
}
Console.WriteLine(String.Format("Network accuracy {0:p}", 1f * rightAnswers / testingDataElementsCount));
Console.Read();
}
