static void Step5()
{
Console.WriteLine("STEP 5: Train neural network...");
NetworkTrainer networkteTrainer = new NetworkTrainer();
networkteTrainer.Train(DataFilesInfoGetter.NetworkFile, DataFilesInfoGetter.NormalizedTrainingFile);
}
static void Main(string[] args)
{
//IList<Tuple<double[], double[]>> trainset = new List<Tuple<double[], double[]>>
//{
// Tuple.Create<double[], double[]>(new double[]{ 97, 98, 97, 98, 97, 98, 97, 98 }, new double[]{ 1.0 / 97, 1.0 / 98 }),
// Tuple.Create<double[], double[]>(new double[]{ 98, 98, 98, 98, 98, 98, 98, 98 }, new double[]{ 1.0 / 98, 1.0 / 98 }),
// Tuple.Create<double[], double[]>(new double[]{ 95, 101, 95, 101, 95, 101, 95, 101 }, new double[]{ 1.0 / 95, 1.0 / 101 }),
// Tuple.Create<double[], double[]>(new double[]{ 120, 121, 120, 121, 120, 121, 120, 121 }, new double[]{ 1.0 / 120, 1.0 / 121 })
//};
IList <Tuple <double[], double[]> > trainset = new List <Tuple <double[], double[]> >
{
Tuple.Create <double[], double[]>(new double[] { 1, 0, 1, 0, 1, 0, 1, 0 }, new double[] { 1, 0 }),
Tuple.Create <double[], double[]>(new double[] { 0, 1, 0, 1, 0, 1, 0, 1 }, new double[] { 0, 1 }),
Tuple.Create <double[], double[]>(new double[] { 0, 0, 0, 0, 0, 0, 0, 0 }, new double[] { 0, 0 }),
Tuple.Create <double[], double[]>(new double[] { 1, 1, 1, 1, 1, 1, 1, 1 }, new double[] { 1, 1 })
};
NeuralNetwork net = NeuralNetwork.GetInstance(inputs: null);
NetworkTrainer.Train(net /*, trainset*/);
NetworkTrainer.Test(net, trainset);
Console.ReadKey();
}
private static void Main(string[] args)
{
var net = new Network();
var trainer = new NetworkTrainer();
var tester = new NetworkTester();
trainer.Train(net);
tester.Test(net);
Console.ReadKey();
}
//static void FuckWithWeights(Network network)
//{
// network.HiddenLayers[0].Neurons[0].Bias = -1.74976547;
// network.HiddenLayers[0].Neurons[0].UpstreamDendrites[0].Weight = 0.22117967;
// network.HiddenLayers[0].Neurons[0].UpstreamDendrites[1].Weight = -1.07004333;
// network.HiddenLayers[0].Neurons[1].Bias = 0.3426804;
// network.HiddenLayers[0].Neurons[1].UpstreamDendrites[0].Weight = -0.18949583;
// network.HiddenLayers[0].Neurons[1].UpstreamDendrites[1].Weight = 0.25500144;
// network.HiddenLayers[0].Neurons[2].Bias = 1.1530358;
// network.HiddenLayers[0].Neurons[2].UpstreamDendrites[0].Weight = -0.45802699;
// network.HiddenLayers[0].Neurons[2].UpstreamDendrites[1].Weight = 0.43516349;
// network.HiddenLayers[0].Neurons[3].Bias = -0.25243604;
// network.HiddenLayers[0].Neurons[3].UpstreamDendrites[0].Weight = -0.58359505;
// network.HiddenLayers[0].Neurons[3].UpstreamDendrites[1].Weight = 0.81684707;
// network.HiddenLayers[0].Neurons[4].Bias = 0.98132079;
// network.HiddenLayers[0].Neurons[4].UpstreamDendrites[0].Weight = 0.67272081;
// network.HiddenLayers[0].Neurons[4].UpstreamDendrites[1].Weight = -0.10441114;
// network.OutputLayer.Neurons[0].Bias = 0.51421884;
// network.OutputLayer.Neurons[0].UpstreamDendrites[0].Weight = -0.53128038;
// network.OutputLayer.Neurons[0].UpstreamDendrites[1].Weight = 1.02973269;
// network.OutputLayer.Neurons[0].UpstreamDendrites[2].Weight = -0.43813562;
// network.OutputLayer.Neurons[0].UpstreamDendrites[3].Weight = -1.11831825;
// network.OutputLayer.Neurons[0].UpstreamDendrites[4].Weight = 1.61898166;
//}
static void Main(string[] args)
{
// What I cannot create, I do not understand.
// ~Richard P. Feynman
ITrainingDataBuilder trainingDataBuilder = new MNISTTrainingDataBuilder();
trainingDataBuilder.BuildTrainingData();
double totalAccuracy = 0.0;
const int NumberOfIterations = 1;
DateTime start = DateTime.Now;
for (int c = 0; c < NumberOfIterations; c++)
{
Random rand = new Random();
Network network = Network.BuildNetwork(
rand,
new Math.CostFunctions.CrossEntropyCostFunction(),
new Math.RegularizationFunctions.L2Normalization(.1),
Common.WeightIntializerType.RandomGaussianWithNeuronCount,
new DropoutLayerOptions(0),
784, 10, 30);
NetworkTrainer networkTrainer = new NetworkTrainer();
networkTrainer.Train(network,
trainingDataBuilder,
.5, 30, 10,
-1,
OnLearningProgress,
OnValidationDataUpdate);
totalAccuracy += trainingDataBuilder.GradeResults(network, trainingDataBuilder.TestData) * 100.0;
}
Console.WriteLine($"Accurancy: {(totalAccuracy / (NumberOfIterations * 1.0)).ToString("000.00")}% in {(DateTime.Now - start).TotalSeconds} seconds.");
return;
}
public void TestTrainXor()
{
var pg = new PositiveUniformParameterGenerator();
var neuronFactor = new NeuronFactory(pg);
var synapseFactory = new SynapseFactory(pg);
var n = NetworkFactory.CreateMultilayerPerceptron(new[] { 2, 2, 1 }, ActivationFunction.Sigmoid,
ActivationFunction.Identity, null, neuronFactor, synapseFactory);
var trainer = new NetworkTrainer(n, 0.9, 0.1);
var examples = new Matrix(new double[, ] {
{ 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 }
});
var labels = new Vector(0, 1, 1, 0);
trainer.Train(examples, labels, 1000);
for (var i = 0; i < labels.Length; i++)
{
var x = examples.GetRow(i);
var y = labels[i];
Console.WriteLine("Actual: {0}, Result: {1}", y, n.Compute(x));
Assert.IsTrue(Math.Abs(y - n.Compute(x)[0]) < 0.01);
}
}
