private static void Train(BasicNetwork network, BasicMLDataSet trainingSet)
{
var propagation = new StochasticGradientDescent(network, trainingSet);
var epoch = 1;
for (var i = 0; i < 200; i++)
{
propagation.Iteration(1000);
Console.WriteLine($"Epoch: {epoch} Error: {propagation.Error}");
epoch++;
}
propagation.FinishTraining();
}
void ItWorks()
{
var network2 = new BasicNetwork();
var data = LoadData();
var label = LoadLabel();
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, 4));
network.AddLayer(new BasicLayer(new ActivationReLU(), true, 8));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
network.Structure.FinalizeStructure();
network.Reset();
var trainingSet = new BasicMLDataSet(data, label);
var propagation = new StochasticGradientDescent(network, trainingSet);
var epoch = 1;
for (var i = 0; i < 5000; i++)
{
propagation.Iteration();
Console.WriteLine($"Epoch: {epoch} Error: {propagation.Error}");
epoch++;
}
propagation.FinishTraining();
var weights = network.Flat.Weights;
var biases = network.Flat.BiasActivation;
foreach (var pair in trainingSet)
{
var output = network.Compute(pair.Input);
Debug.Log(pair.Input[0] + "," + pair.Input[1]
+ ", actual=" + output[0] + ",ideal=" + pair.Ideal[0]);
}
}
