From 89c3571a81987bda237393c4f8f943cb921b0b46 Mon Sep 17 00:00:00 2001 From: inter Date: Thu, 4 Sep 2025 14:09:22 +0800 Subject: [PATCH] Add File --- .../easyai/transFormer/nerve/Nerve.java | 275 ++++++++++++++++++ 1 file changed, 275 insertions(+) create mode 100644 src/main/java/org/dromara/easyai/transFormer/nerve/Nerve.java diff --git a/src/main/java/org/dromara/easyai/transFormer/nerve/Nerve.java b/src/main/java/org/dromara/easyai/transFormer/nerve/Nerve.java new file mode 100644 index 0000000..6ea598e --- /dev/null +++ b/src/main/java/org/dromara/easyai/transFormer/nerve/Nerve.java @@ -0,0 +1,275 @@ +package org.dromara.easyai.transFormer.nerve; + +import org.dromara.easyai.matrixTools.Matrix; +import org.dromara.easyai.config.RZ; +import org.dromara.easyai.i.ActiveFunction; +import org.dromara.easyai.i.OutBack; +import org.dromara.easyai.matrixTools.MatrixList; +import org.dromara.easyai.matrixTools.MatrixOperation; +import org.dromara.easyai.transFormer.LineBlock; +import org.dromara.easyai.transFormer.seflAttention.LayNorm; + +import java.util.*; + +/** + * @author lidapeng + * 神经元，所有类别神经元都要继承的类，具有公用属性 + * @date 9:36 上午 2019/12/21 + */ +public abstract class Nerve { + private final List son = new ArrayList<>();//轴突下一层的连接神经元 + private final List father = new ArrayList<>();//树突上一层的连接神经元 + protected LayNorm beforeLayNorm;//多头自注意力层 + protected LayNorm afterLayNorm;//多头自注意力层 + protected Matrix powerMatrix;//权重矩阵 作为模型取出 + private final int id;//同级神经元编号,注意在同层编号中ID应有唯一性 + private final int hiddenNerveNub;//隐层神经元个数 + private final int sensoryNerveNub;//输入神经元个数 + private final int outNerveNub;//输出神经元个数 + protected Map reMatrixFeatures = new HashMap<>(); + protected String name;//该神经元所属类型 + protected Matrix featureMatrix; + protected float E;//模板期望值 + protected float studyPoint; + protected LineBlock lineBlock;//是否为最后线性层 + protected Matrix sigmaW;//对上一层权重与上一层梯度的积进行求和 + private int backNub = 0;//当前节点被反向传播的次数 + protected ActiveFunction activeFunction; + protected Matrix outMatrix; + protected int myUpNumber;//统计参数数量 + protected int depth;//所处深度 + private final int regularModel;//正则模式 + private final float regular;//正则系数 + private final MatrixOperation matrixOperation; + + public int getDepth() { + return depth; + } + + public void setBeforeLayNorm(LayNorm beforeLayNorm) { + this.beforeLayNorm = beforeLayNorm; + } + + public void setAfterLayNorm(LayNorm afterLayNorm) { + this.afterLayNorm = afterLayNorm; + } + + protected Nerve(int id, String name, float studyPoint, ActiveFunction activeFunction, int sensoryNerveNub, + int hiddenNerveNub, int outNerveNub, LineBlock lineBlock, int regularModel, + float regular, int coreNumber) throws Exception {//该神经元在同层神经元中的编号 + this.id = id; + matrixOperation = new MatrixOperation(coreNumber); + this.regular = regular; + this.regularModel = regularModel; + this.lineBlock = lineBlock; + this.hiddenNerveNub = hiddenNerveNub;//隐层神经元个数 + this.sensoryNerveNub = sensoryNerveNub;//输入神经元个数 + this.outNerveNub = outNerveNub;//输出神经元个数 + this.name = name; + this.studyPoint = studyPoint; + this.activeFunction = activeFunction; + initPower();//生成随机权重 + } + + public float[][] getModel() throws Exception { + return powerMatrix.getMatrix(); + } + + public void insertModel(float[][] modelPower) throws Exception { + for (int i = 0; i < powerMatrix.getX(); i++) { + for (int j = 0; j < powerMatrix.getY(); j++) { + powerMatrix.setNub(i, j, modelPower[i][j]); + } + } + } + + protected void sendMessage(long eventId, Matrix parameter, boolean isStudy, Matrix allFeature, OutBack outBack, + List E, Matrix encoderFeature, boolean outAllPro) throws Exception { + if (!son.isEmpty()) { + for (Nerve nerve : son) { + nerve.input(eventId, parameter, isStudy, allFeature, outBack, E, encoderFeature, outAllPro); + } + } + + } + + + private void backSendMessage(long eventId, Matrix errorMatrix, Matrix allError) throws Exception {//反向传播 + if (!father.isEmpty()) {//要先判定是不是可以继续往后传 + if (errorMatrix.getY() - 1 != father.size()) { + throw new Exception("回传参数数量不一致!"); + } + for (int i = 0; i < father.size(); i++) { + father.get(i).backGetMessage(errorMatrix.getColumn(i), eventId, allError); + } + } else if (lineBlock != null) {//最后线性层 + lineBlock.backError(eventId, errorMatrix); + } else {//从fnn往后传 + afterLayNorm.backErrorFromFNN(errorMatrix, eventId, allError); + } + } + + protected void input(long eventId, Matrix parameter, boolean isStudy, Matrix allFeature, OutBack outBack, + List E, Matrix encoderFeature, boolean outAllPro) throws Exception {//输入参数 + + } + + protected void toOut(long eventId, Matrix parameter, boolean isStudy, OutBack outBack, List E, boolean outAllPro) throws Exception { + + } + + protected void sendOutMessage(long eventId, Matrix parameter, boolean isStudy, OutBack outBack, List E, boolean outAllPro) throws Exception { + if (!son.isEmpty()) { + for (Nerve nerve : son) { + nerve.toOut(eventId, parameter, isStudy, outBack, E, outAllPro); + } + } + } + + private void backGetMessage(Matrix parameter, long eventId, Matrix allError) throws Exception {//反向传播 + backNub++; + if (sigmaW == null) { + sigmaW = parameter; + } else { + sigmaW = matrixOperation.add(sigmaW, parameter); + } + if (backNub == outNerveNub) {//进行新的梯度计算 + backNub = 0; + if (activeFunction != null) { + for (int i = 0; i < sigmaW.getX(); i++) { + float out = outMatrix.getNumber(i, 0); + float value = activeFunction.functionG(out) * sigmaW.getNumber(i, 0); + sigmaW.setNub(i, 0, value); + } + } + updatePower(eventId, sigmaW, allError);//修改阈值 + } + } + + + protected void updatePower(long eventId, Matrix errorMatrix, Matrix allError) throws Exception {//修改阈值 + Matrix myError = matrixOperation.mathMulBySelf(errorMatrix, studyPoint); + Matrix error = updateW(myError, errorMatrix);//更新本神经元参数与返回下层误差 + sigmaW = null;//求和结果归零 + backSendMessage(eventId, error, allError); + } + + private Matrix getRegularizationMatrix() throws Exception { + int size = powerMatrix.getX(); + float sigma = 0; + for (int i = 0; i < size; i++) { + float value = powerMatrix.getNumber(i, 0); + if (regularModel == RZ.L1) {//l1正则化 + sigma = sigma + (float) Math.abs(value); + } else { + sigma = sigma + (float) Math.pow(value, 2); + } + } + float param = (float) sigma * regular * studyPoint; + Matrix rzMatrix = new Matrix(powerMatrix.getX(), powerMatrix.getY()); + for (int i = 0; i < size; i++) { + float value = powerMatrix.getNumber(i, 0); + float re = 0F; + if (regularModel == RZ.L2) { + re = param * -value; + } else if (regularModel == RZ.L1) { + if (value > 0) { + re = -param; + } else if (value < 0) { + re = param; + } + } + rzMatrix.setNub(i, 0, re); + } + return rzMatrix; + } + + private Matrix updateW(Matrix errorMatrix, Matrix error) throws Exception {// + Matrix rzMatrix = null; + if (regularModel != RZ.NOT_RZ) { + rzMatrix = getRegularizationMatrix(); + } + Matrix subFeature = matrixOperation.matrixMulPd(error, featureMatrix, powerMatrix, true); + Matrix subPower = matrixOperation.matrixMulPd(errorMatrix, featureMatrix, powerMatrix, false); + if (regularModel != RZ.NOT_RZ) { + powerMatrix = matrixOperation.add(powerMatrix, rzMatrix);//正则化抑制权重 + } + powerMatrix = matrixOperation.add(powerMatrix, subPower);//更新权重 + return subFeature; + } + + protected boolean insertMatrixParameter(long eventID, Matrix matrix) throws Exception {//reMatrixFeatures + boolean allReady = false; + MatrixList feature; + if (reMatrixFeatures.containsKey(eventID)) { + feature = reMatrixFeatures.get(eventID); + feature.add(matrix); + } else { + feature = new MatrixList(matrix, false); + reMatrixFeatures.put(eventID, feature); + } + if (feature.getY() == myUpNumber) { + allReady = true; + } else if (feature.getY() > myUpNumber) { + throw new Exception("接收矩阵参数数量异常"); + } + return allReady; + } + + protected Matrix opMatrix(Matrix feature, boolean isStudy) throws Exception { + Matrix th = new Matrix(feature.getX(), 1); + for (int i = 0; i < th.getX(); i++) { + th.setNub(i, 0, 1F); + } + Matrix matrix = matrixOperation.pushVector(feature, th, false); + Matrix sigma = matrixOperation.mulMatrix(matrix, powerMatrix); + if (activeFunction != null) { + for (int i = 0; i < sigma.getX(); i++) { + float value = activeFunction.function(sigma.getNumber(i, 0)); + sigma.setNub(i, 0, value); + } + } + if (isStudy) { + featureMatrix = matrix; + outMatrix = sigma; + } + return sigma; + } + + + private void initPower() throws Exception {//初始化权重及阈值 + Random random = new Random(); + //设置初始化权重范围收缩系数 + if (name.equals("HiddenNerve")) {//隐层神经元 + myUpNumber = sensoryNerveNub; + } else if (name.equals("OutNerve")) {//输出神经元 + myUpNumber = hiddenNerveNub; + } else {//softmax + myUpNumber = outNerveNub; + } + if (myUpNumber > 0) {//输入个数 + powerMatrix = new Matrix(myUpNumber + 1, 1); + float sh = (float) Math.sqrt(myUpNumber); + for (int i = 0; i < myUpNumber; i++) { + float nub = (float) (random.nextFloat() / sh); + powerMatrix.setNub(i, 0, nub); + } + //生成随机阈值 + powerMatrix.setNub(myUpNumber, 0, (float) (random.nextFloat() / sh)); + } + } + + public int getId() { + return id; + } + + + public void connect(List nerveList) { + son.addAll(nerveList);//连接下一层 + } + + + public void connectFather(List nerveList) { + father.addAll(nerveList);//连接上一层 + } +}