public void Train_RuningTraining_NetworkIsTrained()
{
var network = new SimpleNeuralNetwork(3);
var layerFactory = new NeuralLayerFactory();
network.AddLayer(layerFactory.CreateNeuralLayer(3, new RectifiedActivationFuncion(), new WeightedSumFunction()));
network.AddLayer(layerFactory.CreateNeuralLayer(1, new SigmoidActivationFunction(0.7), new WeightedSumFunction()));
network.PushExpectedValues(
new double[][] {
new double[] { 0 },
new double[] { 1 },
new double[] { 1 },
new double[] { 0 },
new double[] { 1 },
new double[] { 0 },
new double[] { 0 },
});
network.Train(
new double[][] {
new double[] { 150, 2, 0 },
new double[] { 1002, 56, 1 },
new double[] { 1060, 59, 1 },
new double[] { 200, 3, 0 },
new double[] { 300, 3, 1 },
new double[] { 120, 1, 0 },
new double[] { 80, 1, 0 },
}, 10000);
network.PushInputValues(new double[] { 1054, 54, 1 });
var outputs = network.GetOutput();
}
public override PatternResult Train(List <Pattern> patterns, int iterations)
{
if (patterns.Count == 0)
{
return(null);
}
DoubleVectorList inputs = new DoubleVectorList();
DoubleVectorList outputs = new DoubleVectorList();
foreach (Pattern p in patterns)
{
inputs.Add(p.Inputs.Scale(0.02, 0.98));
outputs.Add(p.Outputs);
}
double error = sann.Train(inputs, outputs, iterations);
return(new PatternResult(error));
}
static void Train_predict_and_save(string filepath)
{
// train & testing data
var training_data = new Dictionary <int, Tuple <float[], float[]> >
{ // ys xs
[0] = new Tuple <float[], float[]>(new float[] { 0, 1 }, new float[] { 1 }),
[1] = new Tuple <float[], float[]>(new float[] { 1, 0 }, new float[] { 1 }),
[2] = new Tuple <float[], float[]>(new float[] { 0, 0 }, new float[] { 0 }),
[3] = new Tuple <float[], float[]>(new float[] { 1, 1 }, new float[] { 0 })
};
var snn = new SimpleNeuralNetwork(2, 0.4f, Activation.FunctionsEnum.Sigmoid);
snn.Add(4);
snn.Add(1);
// train
Console.WriteLine("Entrenamiento:\n");
Random random = new Random();
int j = 0;
Console.WriteLine("Training ...");
for (int i = 0; i < 100000; i++)
{
j = random.Next(4);
snn.Train(training_data[j].Item1, training_data[j].Item2);
}
// predict
Console.WriteLine("\nPredicciones:\n");
for (int i = 0; i < 4; i++)
{
var res = snn.Predict(training_data[i].Item1);
Console.WriteLine(string.Format("xs [ {0}, {1} ] = {2}", training_data[i].Item1[0], training_data[i].Item1[1], res[0]));
}
SimpleNeuralNetwork.Save(snn, filepath);
Console.WriteLine("\nRed Neuronal guardada !!.\n");
}
static void Main(string[] args)
{
var network = new SimpleNeuralNetwork(1);
var layerFactory = new NeuralLayerFactory();
network.AddLayer(layerFactory.CreateNeuralLayer(2, new RectifiedActivationFuncion(), new WeightedSumFunction()));
network.AddLayer(layerFactory.CreateNeuralLayer(1, new SigmoidActivationFunction(0.4), new WeightedSumFunction()));
double[][] expectedValues = new double[samples][];
double[][] trainingValues = new double[samples][];
for (int i = 0; i < samples; i++)
{
Random rng = new Random();
Random rng2 = new Random();
int val1 = rng.Next(rng.Next() % 1000);
int val2 = rng2.Next(i % 900);
expectedValues[i] = new double[] { (val1 + val2) % 2 };
trainingValues[i] = new double[] { val1, val2 };
Console.WriteLine($"val1: {val1} val2: {val2} sum: { (val1 + val2) % 2 }");
}
network.PushExpectedValues(expectedValues);
network.Train(trainingValues, 5000);
network.PushInputValues(new double[] { 1054, 54 });
var outputs = network.GetOutput();
Console.WriteLine($"network output: {string.Join(", ", outputs)}");
Console.ReadKey();
}
public void TrainNetwork_6Inputs_3HiddenLayer_2Outputs()
{
var network = new SimpleNeuralNetwork(6, 1.95); // six input nuerons
var layerFactory = new NeuralLayerFactory();
network.AddLayer(layerFactory.CreateNeuralLayer(3, new SigmoidActivationFunction(0.7), new WeightedSumFunction())); // three hidden layers
network.AddLayer(layerFactory.CreateNeuralLayer(2, new LazyOutputFunction(), new WeightedSumFunction())); // two output layers
network.PushExpectedValues(
new double[][] {
new double[] { 0.25, 0.20 },
new double[] { 0.10, 0.05 },
new double[] { 0.16, 0.30 },
new double[] { 0.30, 0.10 },
new double[] { 0.25, 0.20 },
new double[] { 0.10, 0.05 },
new double[] { 0.16, 0.30 },
new double[] { 0.30, 0.10 },
});
network.Train(
new double[][] {
new double[] { 150, 0, 0, 34, 35, 56 },
new double[] { 190, 23, 56, 0, 29, 529 },
new double[] { 290, 3, 108, 24, 189, 20 },
new double[] { 290, 67, 6, 0, 1, 0 },
new double[] { 150, 0, 0, 34, 35, 56 },
new double[] { 190, 23, 56, 0, 29, 529 },
new double[] { 290, 3, 108, 24, 189, 20 },
new double[] { 290, 67, 6, 0, 1, 0 },
}, 10000);
network.PushInputValues(new double[] { 150, 0, 0, 34, 35, 56 });
var outputs = network.GetOutput();
Console.WriteLine(outputs[0].ToString() + " " + outputs[1].ToString() + "\n\n" + JsonConvert.SerializeObject(network, Formatting.Indented));
}