static void Main(string[] args)
{
double[][] inputs = { new double[] { 0, 0 }, new double[] { 0, 1 }, new double[] { 1, 0 }, new double[] { 1, 1 } };
double[] targets = { 0, 1, 1, 1 };
double error = 1;
int epoch = 0;
ActivationFunction function = new SigmoidFunction();
Neuron neuron = new Neuron(2, function);
LearningStrategy learning = new DeltaRuleLearning(neuron, 0.75);
while (error > 0.01)
{
error = learning.RunEpoch(inputs, targets);
epoch++;
Console.WriteLine("Iteration {0} error: {1}", epoch, error);
}
Console.WriteLine("Training complete after {0} epochs using the Delta Rule learning regime.", epoch);
Console.WriteLine("Testing");
neuron.Update(new double[] { 0, 0 });
Console.WriteLine("{0}", neuron.Output);
neuron.Update(new double[] { 0, 1 });
Console.WriteLine("{0}", neuron.Output);
neuron.Update(new double[] { 1, 0 });
Console.WriteLine("{0}", neuron.Output);
neuron.Update(new double[] { 1, 1 });
Console.WriteLine("{0}", neuron.Output);
}
public Layer(int neuronCount, int inputCount, ActivationFunction function)
{
_inputCount = inputCount;
_neuronCount = neuronCount;
_neurons = new Neuron[neuronCount];
for(int i = 0; i < neuronCount; i++)
{
_neurons[i] = new Neuron(inputCount, function);
}
}
public void NeuronActivation_ReturnsNeuronActivation()
{
ActivationFunction sigmoidFunction = new SigmoidFunction();
Neuron sigmoidNeuron = new Neuron(3, sigmoidFunction);
double[] input = new double[] { 0.3, 0.2, 0.1 };
double expected = 0.05;
sigmoidNeuron[0] = 0.1;
sigmoidNeuron[1] = 0.2;
sigmoidNeuron[2] = 0.3;
sigmoidNeuron.Bias = -0.05;
double actual = sigmoidNeuron.Activation(input);
Assert.AreEqual(expected, actual, 0.0001, "Invalid neuron activation");
}
static void Main(string[] args)
{
double[][] inputs = { new double[] { 0, 0 }, new double[] { 0, 1 }, new double[] { 1, 0 }, new double[] { 1, 1 } };
double[] targets = { 0, 1, 1, 1 };
double error = 1;
int epoch = 0;
ActivationFunction function = new ThresholdFunction();
Neuron neuron = new Neuron(2, function);
LearningStrategy learning = new PerceptronLearning(neuron, 0.25);
while(error > 0)
{
error = learning.RunEpoch(inputs, targets);
epoch++;
logger.InfoFormat("Iteration {0} error: {1}", epoch, error);
}
logger.InfoFormat("Training complete after {0} epochs using the Perceptron learning regime.", epoch);
}
public void NeuronUpdate_UpdatesOutputAndReturnsValue()
{
ActivationFunction sigmoidFunction = new SigmoidFunction();
Neuron sigmoidNeuron = new Neuron(3, sigmoidFunction);
double[] input = new double[] { 0.3, 0.2, 0.1 };
double expected = 0.512497396484;
sigmoidNeuron[0] = 0.1;
sigmoidNeuron[1] = 0.2;
sigmoidNeuron[2] = 0.3;
sigmoidNeuron.Bias = -0.05;
double actual = sigmoidNeuron.Update(input);
double neuronOutput = sigmoidNeuron.Output;
Assert.AreEqual(expected, actual, 0.0001, "Invalid neuron output");
Assert.AreEqual(expected, neuronOutput, 0.0001, "Invalid neuron output");
}
public PerceptronLearning(Neuron neuron, double learningRate)
{
Neuron = neuron;
LearningRate = learningRate;
}
public NeuralConnection(Neuron input_, Neuron output_, float startWage)
{
input = input_;
output = output_;
wage = startWage;
}
