From d7711aa5dc8a3469d0ee9b2207231ba48787eaf7 Mon Sep 17 00:00:00 2001 From: inter Date: Thu, 4 Sep 2025 14:08:44 +0800 Subject: [PATCH] Add File --- .../org/dromara/easyai/nerveEntity/Nerve.java | 458 ++++++++++++++++++ 1 file changed, 458 insertions(+) create mode 100644 src/main/java/org/dromara/easyai/nerveEntity/Nerve.java diff --git a/src/main/java/org/dromara/easyai/nerveEntity/Nerve.java b/src/main/java/org/dromara/easyai/nerveEntity/Nerve.java new file mode 100644 index 0000000..3c2c6ad --- /dev/null +++ b/src/main/java/org/dromara/easyai/nerveEntity/Nerve.java @@ -0,0 +1,458 @@ +package org.dromara.easyai.nerveEntity; + +import org.dromara.easyai.conv.ConvCount; +import org.dromara.easyai.conv.ConvResult; +import org.dromara.easyai.conv.DymStudy; +import org.dromara.easyai.entity.ThreeChannelMatrix; +import org.dromara.easyai.i.CustomEncoding; +import org.dromara.easyai.matrixTools.Matrix; +import org.dromara.easyai.matrixTools.MatrixList; +import org.dromara.easyai.matrixTools.MatrixOperation; +import org.dromara.easyai.config.RZ; +import org.dromara.easyai.i.ActiveFunction; +import org.dromara.easyai.i.OutBack; +import org.dromara.easyai.rnnJumpNerveCenter.CustomManager; + +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 extends ConvCount { + 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 + private Nerve sonOnly; + private Nerve fatherOnly; + protected final float gaMa;//自适应学习率衰减系数 + protected Map dendrites = new HashMap<>();//上一层权重(需要取出) + private final Map dymStudyRate = new HashMap<>();//动态学习率 + protected Map wg = new HashMap<>();//上一层权重与梯度的积 + 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 + protected int upNub;//上一层神经元数量 + protected int downNub;//下一层神经元的数量 + protected Map> features = new HashMap<>();//上一层神经元输入的数值 + protected float threshold;//此神经元的阈值需要取出 + protected String name;//该神经元所属类型 + protected float outNub;//输出数值（ps:只有训练模式的时候才可保存输出过的数值） + protected float E;//模板期望值 + protected float gradient;//当前梯度 + protected float studyPoint; + protected float sigmaW;//对上一层权重与上一层梯度的积进行求和 + protected List sigmaMatrix;//对上层回传误差进行求和 + private int backNub = 0;//当前节点被反向传播的次数 + protected ActiveFunction activeFunction; + private final int rzType;//正则化类型，默认不进行正则化 + private final float lParam;//正则参数 + private final int kernLen;//核长 + protected final int depth;//所处深度 + protected final int matrixX;//卷积输出行数列数 + protected final int matrixY;//卷积输出矩阵列数 + private final MatrixOperation matrixOperation; + protected final int channelNo;//通道数 + private final ConvParameter convParameter = new ConvParameter();//内存中卷积层模型及临时数据 + protected final float oneConvRate; + private final boolean norm;//是否进行1v1卷积升降维 + private final CustomEncoding customEncoding;//自定义编码模块 + private final float gMaxTh; + private final DymStudy dymStudy; + private final boolean auto; + private final float GRate;//梯度衰减 + + public Map getDendrites() { + return dendrites; + } + + public ConvParameter getConvParameter() { + return convParameter; + } + + public void setDendrites(Map dendrites) { + this.dendrites = dendrites; + } + + public float getThreshold() { + return threshold; + } + + public void setThreshold(float threshold) { + this.threshold = threshold; + } + + protected Nerve(int id, int upNub, String name, int downNub, + float studyPoint, boolean init, ActiveFunction activeFunction + , boolean isDynamic, int rzType, float lParam, int kernLen, int depth + , int matrixX, int matrixY, int coreNumber, int channelNo, float onConvRate, boolean norm, CustomEncoding customEncoding + , float gaMa, float gMaxTh, boolean auto, float GRate) throws Exception {//该神经元在同层神经元中的编号 + if (auto) { + if (gaMa <= 0 || gaMa >= 1) { + throw new IllegalArgumentException("gaMa 取值范围是(0,1)，当前值:" + gaMa); + } + if (gMaxTh <= 0) { + throw new IllegalArgumentException("gMaxTh 必须比0大,当前值:" + gMaxTh); + } + } + matrixOperation = new MatrixOperation(coreNumber); + dymStudy = new DymStudy(gaMa, gMaxTh, auto); + this.GRate = GRate; + this.gaMa = gaMa; + this.auto = auto; + this.gMaxTh = gMaxTh; + this.matrixX = matrixX; + this.customEncoding = customEncoding; + this.norm = norm; + this.matrixY = matrixY; + this.channelNo = channelNo; + this.id = id; + this.depth = depth; + this.upNub = upNub; + this.name = name; + this.downNub = downNub; + this.studyPoint = studyPoint; + this.activeFunction = activeFunction; + this.rzType = rzType; + this.lParam = lParam; + this.kernLen = kernLen; + this.oneConvRate = onConvRate; + initPower(init, isDynamic);//生成随机权重 + } + + protected void setStudyPoint(float studyPoint) { + this.studyPoint = studyPoint; + } + + public void sendMessage(long eventId, float parameter, boolean isStudy, Map E + , OutBack outBack) throws Exception { + if (!son.isEmpty()) { + for (Nerve nerve : son) { + nerve.input(eventId, parameter, isStudy, E, outBack); + } + } else { + throw new Exception("this layer is lastIndex"); + } + } + + protected List conv(List matrix) throws Exception {//一次正向卷积，下取样 + return downConvAndPooling(matrix, convParameter, channelNo, activeFunction, kernLen, true, -1); + } + + protected void demRedByMatrixList(long eventId, List matrixList, boolean study, + Map E, OutBack outBack, boolean needMatrix) throws Exception { + if (study) {//训练临时保存 + convParameter.setFeatureMatrixList(matrixList); + } + List feature; + if (norm) { + feature = manyOneConv(matrixList, convParameter.getOneConvPower());//降维后的特征矩阵 + } else { + if (matrixList.size() != 3) { + throw new Exception("不进行维度调节，输入的特征矩阵通道数必须为3"); + } + feature = matrixList; + } + List convMatrix = conv(feature); + sendMatrix(eventId, convMatrix, study, E, outBack, needMatrix); + } + + public void sendMatrixList(long eventId, List parameter, boolean isStudy, + Map E, OutBack outBack) throws Exception { + if (!son.isEmpty()) { + for (Nerve nerve : son) { + nerve.inputMatrixFeature(eventId, parameter, isStudy, E, outBack); + } + } else { + throw new Exception("this layer is lastIndex"); + } + } + + public void sendMatrix(long eventId, List parameter, boolean isStudy, + Map E, OutBack outBack, boolean needMatrix) throws Exception { + if (sonOnly != null) { + sonOnly.inputMatrix(eventId, parameter, isStudy, E, outBack, needMatrix); + } else { + throw new Exception("this layer is lastIndex"); + } + } + + public void sendThreeChannelMatrix(long eventId, ThreeChannelMatrix parameter, boolean isStudy, + Map E, OutBack outBack, boolean needMatrix) throws Exception { + if (sonOnly != null) { + sonOnly.inputThreeChannelMatrix(eventId, parameter, isStudy, E, outBack, needMatrix); + } else { + throw new Exception("this layer is lastIndex"); + } + } + + public void sendListMatrix(long eventId, List parameter, boolean isStudy, + Map E, OutBack outBack, boolean needMatrix) throws Exception { + if (sonOnly != null) { + sonOnly.demRedByMatrixList(eventId, parameter, isStudy, E, outBack, needMatrix); + } else { + throw new Exception("this layer is lastIndex"); + } + } + + private void backSendMessage(long eventId) throws Exception {//反向传播 + if (!father.isEmpty()) { + for (int i = 0; i < father.size(); i++) { + father.get(i).backGetMessage(wg.get(i + 1), eventId); + } + } else if (fatherOnly != null && depth == 1) {//反矩阵误差 + List errorMatrixList = new ArrayList<>(); + int size = this.matrixX * this.matrixY; + int featureSize = wg.size() / size; + for (int i = 0; i < featureSize; ++i) { + List list = new ArrayList<>(); + int startIndex = size * i; + int endIndex = startIndex + size; + for (int j = startIndex; j < endIndex; j++) { + list.add(this.wg.get(j + 1)); + } + Matrix errorMatrix = this.matrixOperation.ListToMatrix(list, this.matrixX, this.matrixY); + errorMatrixList.add(errorMatrix); + } + fatherOnly.backMatrix(errorMatrixList); + } else if (customEncoding != null && depth == 1) {//最后一层返回给第一层 + customEncoding.backError(wg, id); + } + } + + private void backMatrixMessage(List g) throws Exception {//反向传播矩阵 + if (fatherOnly != null) { + fatherOnly.backMatrix(g); + } + } + + protected void input(long eventId, float parameter, boolean isStudy + , Map E, OutBack imageBack) throws Exception {//输入参数 + + } + + protected void inputMatrixFeature(long eventId, List parameters, boolean isStudy + , Map E, OutBack imageBack) throws Exception {//卷积层向网络发送参数 + + } + + protected void inputMatrix(long eventId, List matrix, boolean isKernelStudy, Map E, + OutBack outBack, boolean needMatrix) throws Exception {//输入动态矩阵 + } + + protected void inputThreeChannelMatrix(long eventId, ThreeChannelMatrix picture, boolean isKernelStudy, Map E, + OutBack outBack, boolean needMatrix) throws Exception {//输入动态矩阵 + } + + private void backGetMessage(float parameter, long eventId) throws Exception {//反向传播 + backNub++; + sigmaW = sigmaW + parameter; + if (backNub == downNub) {//进行新的梯度计算 + backNub = 0; + gradient = activeFunction.functionG(outNub) * sigmaW; + updatePower(eventId);//修改阈值 + } + } + + protected void backMatrix(List t) throws Exception {//回传梯度 + backNub++; + if (sigmaMatrix == null) { + sigmaMatrix = t; + } else { + sigmaMatrix = matrixOperation.addMatrixList(t, sigmaMatrix); + } + if (backNub == downNub) { + backNub = 0; + List errorMatrix = backDownPoolingByList(sigmaMatrix, convParameter.getOutX(), convParameter.getOutY());//池化误差返回 + matrixOperation.mathMulByList(errorMatrix, GRate); + List myErrorMatrix = backAllDownConv(convParameter, errorMatrix, studyPoint, activeFunction, channelNo, kernLen, + gaMa, gMaxTh, auto); + sigmaMatrix = null; + if (depth == 1) {//1*1 卷积调整 + if (norm) { + backOneConvByList(myErrorMatrix, convParameter.getFeatureMatrixList(), convParameter.getOneConvPower(), oneConvRate + , convParameter.getOneDymStudyRateList(), gaMa, gMaxTh, auto); + } + } else { + //将梯度继续回传 + backMatrixMessage(myErrorMatrix); + } + } + } + + protected void updatePower(long eventId) throws Exception {//修改阈值 + float thError = dymStudy.getOneValueError(studyPoint, gradient, convParameter); + threshold = threshold - thError; + updateW(gradient, eventId); + sigmaW = 0;//求和结果归零 + backSendMessage(eventId); + } + + private float regularization(float w, float param) {//正则化类型 + float re = 0.0f; + if (rzType != RZ.NOT_RZ) { + if (rzType == RZ.L2) { + re = param * -w; + } else if (rzType == RZ.L1) { + if (w > 0) { + re = -param; + } else if (w < 0) { + re = param; + } + } + } + return re; + } + + private void updateW(float h, long eventId) {//h是学习率 * 当前g（梯度） + List list = features.get(eventId); + float param = 0; + if (rzType != RZ.NOT_RZ) { + float sigma = 0; + for (Map.Entry entry : dendrites.entrySet()) { + if (rzType == RZ.L2) { + sigma = sigma + (float) Math.pow(entry.getValue(), 2); + } else { + sigma = sigma + Math.abs(entry.getValue()); + } + } + param = sigma * lParam * studyPoint; + } + for (Map.Entry entry : dendrites.entrySet()) { + int key = entry.getKey();//上层隐层神经元的编号 + float w = entry.getValue();//接收到编号为KEY的上层隐层神经元的权重 + float bn = list.get(key - 1);//接收到编号为KEY的上层隐层神经元的输入 + float error = dymStudy.getNerveStudyError(dymStudyRate, key, h, studyPoint); + float wp = bn * error; + float dm = w * h; + float regular = regularization(w, param);//正则化抑制权重s + w = w + regular; + w = w + wp; + wg.put(key, dm);//保存上一层权重与梯度的积 + dendrites.put(key, w);//保存修正结果 + } + features.remove(eventId); //清空当前上层输入参数参数 + } + + protected void insertParameters(long eventId, List parameters) {//添加参数 + List featuresList; + if (features.containsKey(eventId)) { + featuresList = features.get(eventId); + } else { + featuresList = new ArrayList<>(); + features.put(eventId, featuresList); + } + featuresList.addAll(parameters); + } + + protected boolean insertParameter(long eventId, float parameter) {//添加参数 + boolean allReady = false; + List featuresList; + if (features.containsKey(eventId)) { + featuresList = features.get(eventId); + } else { + featuresList = new ArrayList<>(); + features.put(eventId, featuresList); + } + featuresList.add(parameter); + if (featuresList.size() >= upNub) { + allReady = true; + } + return allReady; + } + + protected void destoryParameter(long eventId) {//销毁参数 + features.remove(eventId); + } + + protected float calculation(long eventId) throws Exception {//计算当前输出结果 + float sigma = 0; + List featuresList = features.get(eventId); + if (dendrites.size() != featuresList.size()) { + throw new Exception("权重数量:" + dendrites.size() + ",特征数量:" + featuresList.size()); + } + for (int i = 0; i < featuresList.size(); i++) { + float value = featuresList.get(i); + float w = dendrites.get(i + 1); + sigma = w * value + sigma; + } + return sigma - threshold; + } + + private void initPower(boolean init, boolean isDynamic) throws Exception {//初始化权重及阈值 + Random random = new Random(); + if (!isDynamic) {//静态神经元 + //设置初始化权重范围收缩系数 + if (upNub > 0) {//输入个数 + for (int i = 1; i < upNub + 1; i++) { + float nub = 0; + if (init) { + nub = random.nextFloat() / (float) Math.sqrt(upNub); + } + dendrites.put(i, nub);//random.nextFloat() + dymStudyRate.put(i, 0f); + } + //生成随机阈值 + float nub = 0; + if (init) { + nub = random.nextFloat() / (float) Math.sqrt(upNub); + } + threshold = nub; + } + } else {//动态神经元 + initMatrixPower(random); + } + } + + private void initMatrixPower(Random random) throws Exception { + int nerveNub = kernLen * kernLen; + List nerveMatrixList = convParameter.getNerveMatrixList();//一层当中所有的深度卷积核 + List dymStudyRateList = convParameter.getDymStudyRateList(); + List convSizeList = convParameter.getConvSizeList(); + List> onePowers = new ArrayList<>();//1*1卷积核 + List> oneDymStudyRateList = new ArrayList<>(); + for (int k = 0; k < channelNo; k++) {//遍历通道 + Matrix nerveMatrix = new Matrix(nerveNub, 1);//一组通道创建一组卷积核 + convSizeList.add(new ConvSize()); + for (int i = 0; i < nerveMatrix.getX(); i++) {//初始化深度卷积核权重 + float nub = random.nextFloat() / kernLen; + nerveMatrix.setNub(i, 0, nub); + } + nerveMatrixList.add(nerveMatrix); + dymStudyRateList.add(new Matrix(nerveNub, 1)); + if (depth == 1) { + List oneConvPowerList = new ArrayList<>(); + List oneDymStudyRate = new ArrayList<>(); + for (int i = 0; i < 3; i++) { + oneConvPowerList.add(random.nextFloat() / 3); + oneDymStudyRate.add(0f); + } + oneDymStudyRateList.add(oneDymStudyRate); + onePowers.add(oneConvPowerList); + } + } + if (depth == 1) { + convParameter.setOneDymStudyRateList(oneDymStudyRateList); + convParameter.setOneConvPower(onePowers); + } + } + + public int getId() { + return id; + } + + + public void connect(List nerveList) { + son.addAll(nerveList);//连接下一层 + } + + public void connectSonOnly(Nerve nerve) { + sonOnly = nerve; + } + + public void connectFatherOnly(Nerve nerve) { + fatherOnly = nerve; + } + + public void connectFather(List nerveList) { + father.addAll(nerveList);//连接上一层 + } +}