public DataContainer?ComputeLayer(decimal[] Input)
{
if (NeuronObjects != null)
{
for (int i = 0; i < NeuronObjects.Length; i++)
{
GameObject ActivationObj = NeuronObjects[i].transform.Find("UI/Activation").gameObject;
ActivationObj.GetComponent <Text>().text = Input[i].ToString();
}
}
if (IsExecuting)
{
Debug.Log("Trying to Feed Forward but the neural network is executing.");
return(null);
}
m_Input = Input;
IsExecuting = true;
var Result = NextLayer.ComputeLayer(Input);
IsExecuting = false;
return(Result);
}
public DataContainer?ComputeLayer(decimal[] Input)
{
if (NextLayer == null)
{
Debug.LogError(string.Format("Unvalid next layer for Hidden Layer {0}.", LayerName));
return(null);
}
if (Perceptrons == null || Perceptrons.Length == 0)
{
Debug.LogError(string.Format("Hidden Layer {0} has no valid perceptrons, can't get perceptrons values...", LayerName));
return(null);
}
// Check for:
// Activation Function.
// Previous Layers' Input?
int perceptronsLength = Perceptrons.Length;
decimal[] PerceptronValues = new decimal[perceptronsLength];
// For every perceptron, we compute the weighted-sum + bias and with the applied activation function.
for (int i = 0; i < perceptronsLength; i++)
{
PerceptronValues[i] = Perceptrons[i].Compute(Input, ActivationFunction);
}
// Cosmetics at the end.
if (NeuronObjects != null)
{
for (int i = 0; i < Perceptrons.Length; i++)
{
GameObject NeuronObj = Perceptrons[i].NeuronObj;
for (int j = 0; j < PreviousLayer.GetNeuronObjects().Length; j++)
{
GameObject OtherNeuronObj = PreviousLayer.GetNeuronObjects()[j];
LineRenderer lr = Perceptrons[i].GetConnectionLiner(j);
Perceptrons[i].UpdateActivationText();
lr.SetPositions(new Vector3[] { NeuronObj.transform.position, OtherNeuronObj.transform.position });
}
}
}
return(NextLayer.ComputeLayer(GetInput()));
}
