void TrainNetwork()
{
while (true)
{
if (network == null)
{
network = new NeuralNetwork.NetworkModels.Network(numInputs, hiddenLayer, numOutputs);
network.LearnRate = learningRate;
network.Momentum = momentum;
}
Debug.Log("Wait, network is training, this can take a while...");
if (trainType == TrainingType.Epoch)
{
network.Train(dataSets, epochs);
}
else
{
network.Train(dataSets, minimumError);
}
Debug.Log("Training complete!" + " Time elapsed = ");
trained = true;
break;
}
}
// Use this for initialization
void Start()
{
// Setting up variables
trainer.initalize();
_numInputParameters = inputs.Count;
// Initialization of network
_network = new NeuralNetwork.NetworkModels.Network(
_numInputParameters,
NeuronsInHiddenLayers,
_numOutputParameters);
// Train the network
if (UseEpoch)
{
_network.Train(trainer.getTraningData(), MaxEpoch);
}
else
{
_network.Train(trainer.getTraningData(), MinimumError);
}
// Shuffle initial board
foreach (Input i in inputs)
{
if (Random.Range(0f, 2f) <= 1f)
{
i.button();
}
}
output.set(0d, 1d);
}
public double[] Predict(double[] values)
{
if (trained)
{
double[] results = network.Compute(values);
//Debug.Log (results [1]);
return(new double[] { (results [0] * 60 - 30) }); //, results [1] });
}
else
{
return new double[] { 0 }
}; //, 0 };
}
void TrainNetwork()
{
while (true)
{
if (network == null)
{
network = new NeuralNetwork.NetworkModels.Network(numInputs, hiddenLayer, numOutputs);
network.LearnRate = learningRate;
network.Momentum = momentum;
}
Debug.Log("Network is training, this can take a while...");
if (trainType == TrainingType.Epoch)
{
network.Train(dataSets, epochs);
}
else
{
network.Train(dataSets, minimumError);
}
Debug.Log("Training complete!");
trained = true;
break;
}
}