NanoBrain/NanoBrain-unitypackage
NanoBrain/NanoBrain-unitypackage
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Containment training added

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This commit is contained in:
Pascal Serrarens 2026-08-07 09:55:07 +02:00
parent 0541a08c68
commit fb51cc8914
2 changed files with 53 additions and 37 deletions
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17
Runtime/Scripts/Core/Neuron.cs
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@ -695,15 +695,22 @@ namespace NanoBrain {
}
public void BackPropagation3D(Vector3 derivative, float learningRate) {
float derivativeMagnitude = derivative.magnitude;
if (derivativeMagnitude < 1e-03f)
// Small changes are not processed
// We are probably very close to the desired output
return;
foreach (Synapse synapse in this.synapses)
// synapse.BackPropagation(this, derivative, learningRate);
synapse.BackPropagation3D(this, derivative, learningRate);
// As the weight cannot change the direction of the derivative
// we can use the simpler, 1D backpropagation here
// But we still need to determine the sign of the derivative
if (Synapse.AreOpposed(derivative, synapse.neuron.activation))
synapse.BackPropagation(this, -derivative.magnitude, learningRate);
else
synapse.BackPropagation(this, derivative.magnitude, learningRate);
// if (Synapse.AreOpposed(derivative, synapse.neuron.activation))
// synapse.BackPropagation(this, -derivativeMagnitude, learningRate);
// else
// synapse.BackPropagation(this, derivativeMagnitude, learningRate);
// Bias
if (this.trainableBias) {

73
Runtime/Scripts/Core/Synapse.cs
View File

@ -64,37 +64,46 @@ namespace NanoBrain {
}
}
public virtual void BackPropagation(Neuron receiver, Vector3 derivative, float learningRate) {
switch (receiver.activator) {
case Neuron.ActivationType.Linear:
derivative *= 1;
break;
case Neuron.ActivationType.Power:
// untested
derivative *= 2 * math.length(this.neuron.combination);
break;
case Neuron.ActivationType.Reciprocal:
// untested
derivative *= -1 / Mathf.Pow(math.length(this.neuron.combination), 2);
break;
default:
Debug.Log("other activator");
break;
}
public virtual void BackPropagation3D(Neuron receiver, Vector3 derivative, float learningRate) {
// As the weight cannot change the direction of the derivative
// we can use the simpler, 1D backpropagation here
// But we still need to determine the sign of the derivative
this.neuron.BackPropagation3D(derivative * this.weight, learningRate);
if (Synapse.AreOpposed(derivative, this.neuron.activation))
BackPropagation(receiver, -derivative.magnitude, learningRate);
else
BackPropagation(receiver, derivative.magnitude, learningRate);
derivative *= math.length(this.neuron.activation);
// switch (receiver.activator) {
// case Neuron.ActivationType.Linear:
// derivative *= 1;
// break;
// case Neuron.ActivationType.Power:
// // untested
// derivative *= 2 * math.length(this.neuron.combination);
// break;
// case Neuron.ActivationType.Reciprocal:
// // untested
// derivative *= -1 / Mathf.Pow(math.length(this.neuron.combination), 2);
// break;
// default:
// Debug.Log("other activator");
// break;
// }
if (this.trainable) {
float deltaWeight = learningRate * derivative.magnitude;
// Compared to the 1D solution, this does not decrease the weight because magnitude is always positive
// derivative.direction and derivative.sign are different....
if (AreOpposed(derivative, this.neuron.activation))
this.weight -= deltaWeight;
else
this.weight += deltaWeight;
}
// this.neuron.BackPropagation3D(derivative * this.weight, learningRate);
// derivative *= math.length(this.neuron.activation);
// if (this.trainable) {
// float deltaWeight = learningRate * derivative.magnitude;
// // Compared to the 1D solution, this does not decrease the weight because magnitude is always positive
// // derivative.direction and derivative.sign are different....
// if (AreOpposed(derivative, this.neuron.activation))
// this.weight -= deltaWeight;
// else
// this.weight += deltaWeight;
// }
}
public static bool AreOpposed(Vector3 a, Vector3 b) {
@ -105,10 +114,10 @@ namespace NanoBrain {
[Serializable]
public class SynapseData {
public string clusterName;
public string neuronName;
public float weight;
public bool trainable;
public string clusterName;
public string neuronName;
public float weight;
public bool trainable;
public SynapseData(Synapse synapse) {
this.clusterName = synapse.neuron.parent.name;