public void train(CY_NN_Dataset set)
{
CY_NeuronLayerLin inputLayer = new CY_NeuronLayerLin(nrInputs);
CY_NeuronLayerTanh hiddenLayer = new CY_NeuronLayerTanh(nrHiddenLayerNodes);
CY_NeuronLayerLin outputLayer = new CY_NeuronLayerLin(nrOutputs);
new CY_NeuronToNeuronBackprop(inputLayer, hiddenLayer).initFunction = new CY_Init_RND(0d, 0.3d);
new CY_NeuronToNeuronBackprop(hiddenLayer, outputLayer).initFunction = new CY_Init_RND(0d, 0.3d);
network = new CY_NN_Backprop(inputLayer, outputLayer);
network.SetLearningRate(0.1);
network.Learn(set, 1000);
nrTrainings++;
}
// Use this for initialization
void Start()
{
double[] CYNN_Input = new double[] { 1, 2, 3, 4, 5, 6, 7 };
double[] CYNN_Output = new double[] { -4, -2, 0, 2, 4, 6, 8 };
// scaling:
double min = CYNN_Output.Min();
for (int i = 0; i < CYNN_Output.Length; i++)
{
CYNN_Output[i] = CYNN_Output[i] - min;
}
double max = CYNN_Output.Max();
for (int i = 0; i < CYNN_Output.Length; i++)
{
CYNN_Output[i] = CYNN_Output[i] / max;
}
CY_NN_Dataset set = new CY_NN_Dataset();
for (int i = 0; i < CYNN_Input.Count(); i++)
{
set.trainingSamples.Add(new CY_NN_Dataset_Entry(new double[] { CYNN_Input[i] }, new double[] { CYNN_Output[i] }));
}
int variant = 2;
// Variant 1:
if (variant == 1)
{
// Train:
CY_NN_Backprop network;
CY_NeuronLayerLin inputLayer = new CY_NeuronLayerLin(1);
CY_NeuronLayerTanh hiddenLayer = new CY_NeuronLayerTanh(10);
CY_NeuronLayerTanh hiddenLayer2 = new CY_NeuronLayerTanh(10);
CY_NeuronLayerTanh hiddenLayer3 = new CY_NeuronLayerTanh(10);
CY_NeuronLayerLin outputLayer = new CY_NeuronLayerLin(1);
new CY_NeuronToNeuronBackprop(inputLayer, hiddenLayer).initFunction = new CY_Init_RND(0d, 0.3d);
new CY_NeuronToNeuronBackprop(hiddenLayer, hiddenLayer2).initFunction = new CY_Init_RND(0d, 0.3d);
new CY_NeuronToNeuronBackprop(hiddenLayer2, hiddenLayer3).initFunction = new CY_Init_RND(0d, 0.3d);
new CY_NeuronToNeuronBackprop(hiddenLayer3, outputLayer).initFunction = new CY_Init_RND(0d, 0.3d);
network = new CY_NN_Backprop(inputLayer, outputLayer);
network.SetLearningRate(0.1);
network.Learn(set, 1000);
// Test:
for (double xVal = 1; xVal < 8; xVal++)
{
var res = (network.Run(new double[] { xVal })[0]);
res = res * max;
res = res + min;
Debug.Log(xVal + ": " + res);
}
Debug.Log("error: " + network.mse);
}
// -------------------------------------------------------------
// Variant 2 (Node has to be configured in the current Unity scene)
// Click on the "CY_ Neural Network Node" prefab once it is in the scene, then "Open in CY-Editor"
// Try e.g. a 1-10-1 network for the input data above
if (variant == 2)
{
// Train:
CY_NeuralNetworkNode NNNode = GameObject.Find("CY_ Neural Network Node").GetComponent <CY_NeuralNetworkNode>();
NNNode.train(set);
// Test:
for (double xVal = 1; xVal < 8; xVal++)
{
var res = (NNNode.run(new double[] { xVal })[0]);
res = res * max;
res = res + min;
Debug.Log(xVal + ": " + res);
}
Debug.Log("error: " + NNNode.getPerformance());
// Statistics like error/performance, the number of calls or trainings etc are also displayed in the "CY-Editor".
}
}