public void SaveCurrentNetworkAsObject()
{
if (FFANN == null)
{
return;
}
networkObject.ann = FFANN;
FFANN = null;
}
public void CreateANN()
{
isFFANNCreated = true;
if (networkObject != null)
{
FFANN = networkObject.ann;
if (isVisualizingANN)
{
try {
visualizationHandler.CreateVisualization(FFANN.GetLayers()[0].GetNeurons().Count, FFANN.GetLayers()[1].GetNeurons().Count, FFANN.GetLayers().Count - 2,
FFANN.GetLayers()[FFANN.GetLayers().Count - 1].GetNeurons().Count, FFANN.GetLayers());
} catch (NullReferenceException e) {
Debug.LogError(e);
}
for (int i = 0; i < FFANN.GetLayers().Count; i++)
{
for (int j = 0; j < FFANN.GetLayers()[i].GetNeurons().Count; j++)
{
FFANN.GetLayers()[i].GetNeurons()[j].CallNeuronVisualUpdateEvent();
}
}
}
}
else
{
if (ANNData != null)
{
inputs = new List <List <double> >(ANNData.CreateInputs());
desiredOutputs = new List <List <double> >(ANNData.CreateDesiredOutputs());
}
FFANN = new FeedForwardArtificialNeuralNetwork(epochs, alpha, numberOfHiddenLayers, numberOfHiddenNeurons, inputs, desiredOutputs,
hiddenLayerActivationFunction, outputLayerActivationFunction, isDelayingExecution);
if (isVisualizingANN)
{
try {
visualizationHandler.CreateVisualization(inputs.Count, numberOfHiddenNeurons, numberOfHiddenLayers, desiredOutputs.Count, FFANN.GetLayers());
} catch (NullReferenceException e) {
Debug.LogError(e);
}
}
}
}
