71 lines
2.2 KiB
C#
71 lines
2.2 KiB
C#
using System;
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using UnityEngine;
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using Unity.Mathematics;
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namespace NanoBrain {
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/// <summary>
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/// A Synapse connects the ouput of a Neuron to another Neuron
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/// </summary>
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[Serializable]
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public class Synapse {
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/// <summary>
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/// The neuron from which input is received
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/// </summary>
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[SerializeReference]
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[HideInInspector]
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public Neuron neuron;
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/// <summary>
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/// The weight value to apply to the Neuron input
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/// </summary>
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public float weight;
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/// <summary>
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/// Indicator whether the weight can be trained
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/// </summary>
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public bool trainable = false;
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/// <summary>
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/// Create a new Synapse
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/// </summary>
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/// <param name="nucleus">The neuron from which input is received</param>
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/// <param name="weight">The weight value to apply to the Neuron input</param>
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public Synapse(Neuron nucleus, float weight = 1.0f) {
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this.neuron = nucleus;
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this.weight = weight;
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}
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public virtual void BackPropagation(Neuron receiver, float error, float learningRate) {
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float derivative = error;
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switch (receiver.activator) {
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case Neuron.ActivationType.Linear:
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derivative *= 1;
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break;
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case Neuron.ActivationType.Power:
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// untested
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derivative *= 2 * math.length(this.neuron.combination);
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break;
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case Neuron.ActivationType.Reciprocal:
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// untested
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derivative *= -1 / Mathf.Pow(math.length(this.neuron.combination), 2);
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break;
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default:
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Debug.Log("other activator");
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break;
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}
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this.neuron.BackPropagation2(derivative * this.weight, learningRate);
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derivative *= math.length(this.neuron.activation);
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if (this.trainable) {
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float deltaWeight = learningRate * derivative;
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this.weight += deltaWeight;
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}
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}
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}
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} |