void SelfTrainNetwork()
{
_NetSelfTrain = true;
var inputlayer = new DenseLayer(10, 4, Activation.Sigmoid());
var hiddenlayer1 = new DenseLayer(inputlayer, Activation.Sigmoid(), 100, LayerType.Hidden);
var hiddenlayer2 = new DenseLayer(hiddenlayer1, Activation.Sigmoid(), 20, LayerType.Hidden);
var outputlayer = new DenseLayer(hiddenlayer2, Activation.Linear(), 9, LayerType.Output);
Network = outputlayer;
Network.Initilize();
_NetSelfTrain = true;
}
IEnumerator TrainNetwork()
{
var inputlayer = new DenseLayer(10, 4, Activation.ReLU());
var hiddenlayer1 = new DenseLayer(inputlayer, Activation.ReLU(), 30, LayerType.Hidden);
var hiddenlayer2 = new DenseLayer(hiddenlayer1, Activation.ReLU(), 500, LayerType.Hidden);
var outputlayer = new DenseLayer(hiddenlayer1, Activation.TangesHyperbolic(), 2, LayerType.Output);
Network = outputlayer;
outputlayer.Initilize();
var trainingdata = GenerateTrainingData();
int epoch = 0;
int epochsize = trainingdata.GetLength(0);
var epocherror = float.MaxValue;
yield return(0);
while (epocherror > 10 && _Training)
{
yield return(0);
epocherror = 0f;
epoch++;
for (var t = 0; t < epochsize; t++)
{
var truth = trainingdata[t, 1];
var input = trainingdata[t, 0];
var output = (Tensor1D)outputlayer.Forward(input);
var dif = output - truth;
var sq = dif * dif;
epocherror += (float)Math.Pow(sq.ElementSum(), 2);
outputlayer.Backward(dif);
}
ErrorText.text = epocherror.ToString();
}
ErrorText.text = ("Finished!");
_Training = false;
}
