static void Main(string[] args)
{
var neuralNetwork = new NeuralNetwork(3, 1, 3, 1, 1);
neuralNetwork.Backpropagate(LearningInputs, LearningOutputs, 100000, 0.00001);
Console.WriteLine("Enter values to test the neural network. Enter the values and operation value separated by space.");
Console.WriteLine("Operation values: AND - -1, OR - 0, XOR - 1");
while (true)
{
var input = Console.ReadLine().Split(' ').Select(Double.Parse).ToList();
neuralNetwork.ProcessInputs(input);
var outputs = neuralNetwork.GetOutputs();
Console.WriteLine(String.Join(" ", outputs.ToArray()));
}
}
static void Main(string[] args)
{
// create a network
NeuralNetwork _network = new NeuralNetwork(2, 2, 5, 1);
Program program = new Program();
// dataset
// a set for learning the logical AND gate
DataSet trainingData = new DataSet();
trainingData.Inputs.Add(new List <double> {
0, 0
});
trainingData.Outputs.Add(new List <double> {
0
});
trainingData.Inputs.Add(new List <double> {
0, 1
});
trainingData.Outputs.Add(new List <double> {
0
});
trainingData.Inputs.Add(new List <double> {
1, 0
});
trainingData.Outputs.Add(new List <double> {
0
});
trainingData.Inputs.Add(new List <double> {
1, 1
});
trainingData.Outputs.Add(new List <double> {
1
});
// train
for (int i = 0; i < 100000; i++)
{
double og_error = 0.0;
double test_error = 0.0;
// calculate current network error
for (int j = 0; j < trainingData.Inputs.Count; j++)
{
_network.SetInputs(trainingData.Inputs[j]);
_network.ForwardPropagate();
List <double> Outputs = _network.GetOutputs();
for (int k = 0; k < Outputs.Count; k++)
{
og_error += Math.Pow(trainingData.Outputs[j][k] - Outputs[k], 2); // squared error cost function
}
}
// create a new network based off of the last one with gaussian distribution
NeuralNetwork _testNetwork = new NeuralNetwork(_network);
// change hidden layer Weights and biases
foreach (Layer layer in _testNetwork.HiddenLayers)
{
foreach (Neuron neuron in layer.Neurons)
{
for (int j = 0; j < neuron.Weights.Count; j++)
{
neuron.Weights[j] += program.NextGaussian(0, 0.025);
}
neuron.Bias += program.NextGaussian(0, 0.05);
}
}
// change output layer Weights and biases
foreach (Neuron neuron in _testNetwork.OutputLayer.Neurons)
{
for (int j = 0; j < neuron.Weights.Count; j++)
{
neuron.Weights[j] += program.NextGaussian(0, 0.5);
}
neuron.Bias += program.NextGaussian(0, 0.5);
}
// calculate test network error
for (int j = 0; j < trainingData.Inputs.Count; j++)
{
_testNetwork.SetInputs(trainingData.Inputs[j]);
_testNetwork.ForwardPropagate();
List <double> Outputs = _testNetwork.GetOutputs();
for (int k = 0; k < Outputs.Count; k++)
{
test_error += Math.Pow(trainingData.Outputs[j][k] - Outputs[k], 2); // squared error cost function
}
}
// if test has less error then replace the network
Console.SetCursorPosition(0, 0);
Console.WriteLine("og_error: " + og_error);
Console.WriteLine("test_error: " + test_error);
if (test_error < og_error)
{
Console.WriteLine("updated");
_network = _testNetwork;
}
else
{
Console.WriteLine("not updated");
}
}
// test the network
while (true)
{
List <double> test_inputs = new List <double>();
for (int i = 0; i < _network.InputLayer.Width; i++)
{
Console.WriteLine("input" + i + ": ");
double one = Convert.ToDouble(Console.ReadLine());
test_inputs.Add(one);
}
_network.SetInputs(test_inputs);
_network.ForwardPropagate();
foreach (double x in _network.GetOutputs())
{
Console.WriteLine("output: " + x);
}
}
}
