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src/main/java/org/dromara/easyai/rnnNerveEntity/Nerve.java
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280
src/main/java/org/dromara/easyai/rnnNerveEntity/Nerve.java
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package org.dromara.easyai.rnnNerveEntity;
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import org.dromara.easyai.matrixTools.Matrix;
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import org.dromara.easyai.matrixTools.MatrixOperation;
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import org.dromara.easyai.config.RZ;
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import org.dromara.easyai.i.ActiveFunction;
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import org.dromara.easyai.i.OutBack;
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import java.util.*;
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/**
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* @author lidapeng
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* 神经元，所有类别神经元都要继承的类，具有公用属性
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* @date 9:36 上午 2019/12/21
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*/
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public abstract class Nerve {
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private final List<Nerve> son = new ArrayList<>();//轴突下一层的连接神经元
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private final List<Nerve> rnnOut = new ArrayList<>();//rnn隐层输出神经元集合
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private final List<Nerve> father = new ArrayList<>();//树突上一层的连接神经元
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protected Map<Integer, Float> dendrites = new HashMap<>();//上一层权重(需要取出)
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protected Map<Integer, Float> wg = new HashMap<>();//上一层权重与梯度的积
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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
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boolean fromOutNerve = false;//是否是输出神经元
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protected int upNub;//上一层神经元数量
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protected int downNub;//下一层神经元的数量
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protected int rnnOutNub;//rnn输出神经元数量
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protected Map<Long, List<Float>> features = new HashMap<>();//上一层神经元输入的数值
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protected Matrix nerveMatrix;//权重矩阵可获取及注入
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protected float threshold;//此神经元的阈值需要取出
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protected String name;//该神经元所属类型
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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）
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protected float E;//模板期望值
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protected float gradient;//当前梯度
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protected float studyPoint;
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protected float sigmaW;//对上一层权重与上一层梯度的积进行求和
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private int backNub = 0;//当前节点被反向传播的次数
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protected ActiveFunction activeFunction;
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private final int rzType;//正则化类型，默认不进行正则化
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private final float lParam;//正则参数
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private final Map<Long, Integer> embeddingIndex = new HashMap<>();//记录词向量下标位置
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public Map<Integer, Float> getDendrites() {
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return dendrites;
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}
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public Matrix getNerveMatrix() {
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return nerveMatrix;
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}
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public void setNerveMatrix(Matrix nerveMatrix) {
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this.nerveMatrix = nerveMatrix;
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}
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public void setDendrites(Map<Integer, Float> dendrites) {
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this.dendrites = dendrites;
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}
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public float getThreshold() {
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return threshold;
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}
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public void setThreshold(float threshold) {
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this.threshold = threshold;
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}
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protected Nerve(int id, int upNub, String name, int downNub,
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float studyPoint, boolean init, ActiveFunction activeFunction, int rzType, float lParam, int rnnOutNub) throws Exception {//该神经元在同层神经元中的编号
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this.id = id;
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this.upNub = upNub;
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this.name = name;
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this.downNub = downNub;
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this.studyPoint = studyPoint;
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this.activeFunction = activeFunction;
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this.rzType = rzType;
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this.lParam = lParam;
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this.rnnOutNub = rnnOutNub;
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if (name.equals("OutNerve")) {
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fromOutNerve = true;
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}
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initPower(init);//生成随机权重
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}
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protected void setStudyPoint(float studyPoint) {
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this.studyPoint = studyPoint;
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}
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public void sendMessage(long eventId, float parameter, boolean isStudy, Map<Integer, Float> E
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, OutBack outBack, boolean isEmbedding, Matrix rnnMatrix) throws Exception {
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if (!son.isEmpty()) {
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for (Nerve nerve : son) {
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nerve.input(eventId, parameter, isStudy, E, outBack, isEmbedding, rnnMatrix);
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}
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} else {
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throw new Exception("this layer is lastIndex");
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}
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}
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public void sendRnnMessage(long eventId, float parameter, boolean isStudy, Map<Integer, Float> E
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, OutBack outBack, boolean isEmbedding, Matrix rnnMatrix) throws Exception {
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if (!rnnOut.isEmpty()) {
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for (Nerve nerve : rnnOut) {
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nerve.input(eventId, parameter, isStudy, E, outBack, isEmbedding, rnnMatrix);
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}
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} else {
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throw new Exception("this layer is lastIndex");
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}
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}
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private void backSendMessage(long eventId, boolean fromOutNerve) throws Exception {//反向传播
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if (!father.isEmpty()) {
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for (int i = 0; i < father.size(); i++) {
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father.get(i).backGetMessage(wg.get(i + 1), eventId, fromOutNerve);
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}
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}
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}
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protected void input(long eventId, float parameter, boolean isStudy
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, Map<Integer, Float> E, OutBack imageBack, boolean isEmbedding, Matrix rnnMatrix) throws Exception {//输入参数
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}
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private void backGetMessage(float parameter, long eventId, boolean fromOutNerve) throws Exception {//反向传播
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backNub++;
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//sigmaW = ArithUtil.add(sigmaW, parameter);
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sigmaW = sigmaW + parameter;
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int number;
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if (fromOutNerve) {
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number = rnnOutNub;
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} else {
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number = downNub;
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}
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if (backNub == number) {//进行新的梯度计算
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backNub = 0;
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gradient = activeFunction.functionG(outNub) * sigmaW;
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updatePower(eventId);//修改阈值
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}
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}
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protected void updatePower(long eventId) throws Exception {//修改阈值
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//float h = ArithUtil.mul(gradient, studyPoint);//梯度下降
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float h = gradient * studyPoint;
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//threshold = ArithUtil.add(threshold, -h);//更新阈值
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threshold = threshold - h;
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updateW(h, eventId);
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sigmaW = 0;//求和结果归零
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backSendMessage(eventId, fromOutNerve);
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}
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private float regularization(float w, float param) {//正则化类型
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float re = 0.0F;
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if (rzType != RZ.NOT_RZ) {
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if (rzType == RZ.L2) {
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//re = ArithUtil.mul(param, -w);
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re = param * -w;
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} else if (rzType == RZ.L1) {
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if (w > 0) {
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re = -param;
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} else if (w < 0) {
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re = param;
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}
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}
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}
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return re;
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}
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private void updateW(float h, long eventId) {//h是学习率 * 当前g（梯度）
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List<Float> list = features.get(eventId);
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float param = 0;
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if (rzType != RZ.NOT_RZ) {
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double sigma = 0;
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for (Map.Entry<Integer, Float> entry : dendrites.entrySet()) {
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if (rzType == RZ.L2) {
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sigma = sigma + (float) Math.pow(entry.getValue(), 2);
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} else {
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sigma = sigma + (float) Math.abs(entry.getValue());
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}
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}
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param = (float) sigma * lParam * studyPoint;
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}
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for (Map.Entry<Integer, Float> entry : dendrites.entrySet()) {
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int key = entry.getKey();//上层隐层神经元的编号
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float w = entry.getValue();//接收到编号为KEY的上层隐层神经元的权重
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float bn = list.get(key - 1);//接收到编号为KEY的上层隐层神经元的输入
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float wp = bn * h;
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float dm = w * gradient;
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float regular = regularization(w, param);//正则化抑制权重s
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w = w + regular;
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w = w + wp;
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wg.put(key, dm);//保存上一层权重与梯度的积
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dendrites.put(key, w);//保存修正结果
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}
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features.remove(eventId); //清空当前上层输入参数参数
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}
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protected boolean insertParameter(long eventId, float parameter, boolean embedding) {//添加参数
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boolean allReady = false;
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List<Float> featuresList;
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if (features.containsKey(eventId)) {
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featuresList = features.get(eventId);
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} else {
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featuresList = new ArrayList<>();
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features.put(eventId, featuresList);
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}
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if (embedding && parameter > 0.5) {
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embeddingIndex.put(eventId, featuresList.size());
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}
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featuresList.add(parameter);
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if (featuresList.size() >= upNub) {
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allReady = true;
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}
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return allReady;
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}
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protected void destoryParameter(long eventId) {//销毁参数
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features.remove(eventId);
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}
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protected float getWOne(long eventId) {
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int index = embeddingIndex.get(eventId);
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return dendrites.get(index + 1);
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}
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protected float calculation(long eventId, boolean isEmbedding) {//计算当前输出结果
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float sigma = 0;
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List<Float> featuresList = features.get(eventId);
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if (!isEmbedding) {
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for (int i = 0; i < featuresList.size(); i++) {
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float value = featuresList.get(i);
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float w = dendrites.get(i + 1);//当value不为0的时候把w取出来
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sigma = w * value + sigma;
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}
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} else {
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int index = embeddingIndex.get(eventId);
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sigma = featuresList.get(index) * dendrites.get(index + 1);
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embeddingIndex.remove(eventId);
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}
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return sigma - threshold;//ArithUtil.sub(sigma, threshold);
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}
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private void initPower(boolean init) {//初始化权重及阈值
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Random random = new Random();
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if (upNub > 0) {//输入个数
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float sh = (float) Math.sqrt(upNub);
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for (int i = 1; i < upNub + 1; i++) {
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float nub = 0;
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if (init) {
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nub = random.nextFloat() / sh;
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}
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dendrites.put(i, nub);//random.nextFloat()
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}
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//生成随机阈值
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float nub = 0;
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if (init) {
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nub = random.nextFloat() / sh;
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}
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threshold = nub;
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}
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}
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public int getId() {
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return id;
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}
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public void connect(List<Nerve> nerveList) {
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son.addAll(nerveList);//连接下一层
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}
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public void connectOut(List<Nerve> nerveList) {
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rnnOut.addAll(nerveList);
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}
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public void connectFather(List<Nerve> nerveList) {
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father.addAll(nerveList);//连接上一层
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}
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}
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