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src/main/java/org/dromara/easyai/rnnJumpNerveEntity/Nerve.java
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385
src/main/java/org/dromara/easyai/rnnJumpNerveEntity/Nerve.java
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package org.dromara.easyai.rnnJumpNerveEntity;
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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 Map<Integer, List<Nerve>> son = new HashMap<>();//轴突下一层的连接神经元
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private final Map<Integer, List<Nerve>> father = new HashMap<>();//树突上一层的连接神经元
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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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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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private final int hiddenNerveNub;//隐层神经元个数
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private final int sensoryNerveNub;//输入神经元个数
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private final int outNerveNub;//输出神经元个数
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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 int myUpNumber;//统计参数数量
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protected int depth;//所处深度
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protected int allDepth;//总深度
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protected boolean creator;//是否为创建网络
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protected int startDepth;//开始深度
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public int getDepth() {
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return depth;
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}
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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, String name,
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float studyPoint, boolean init, ActiveFunction activeFunction, int rzType, float lParam, int sensoryNerveNub
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, int hiddenNerveNub, int outNerveNub, int allDepth, boolean creator, int startDepth) 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.creator = creator;
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this.startDepth = startDepth;
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this.allDepth = allDepth;
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this.hiddenNerveNub = hiddenNerveNub;//隐层神经元个数
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this.sensoryNerveNub = sensoryNerveNub;//输入神经元个数
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this.outNerveNub = outNerveNub;//输出神经元个数
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this.name = name;
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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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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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private int getNextStorey(int[] storeys, int index) {
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int nextStorey = -1;
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int nextIndex = index + 1;
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if (storeys.length > nextIndex) {//可以继续前进
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nextStorey = storeys[nextIndex];
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}
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return nextStorey;
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}
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protected void sendSoftMaxBack(long eventId, float parameter, Matrix rnnMatrix, OutBack outBack, String myWord) throws Exception {
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if (!son.isEmpty()) {
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List<Nerve> nerverList = son.get(0);
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for (Nerve nerve : nerverList) {
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nerve.sendAppointSoftMax(eventId, parameter, rnnMatrix, outBack, myWord);
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}
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} else {
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throw new Exception("this storey is lastIndex");
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}
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}
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protected void sendSoftMax(long eventId, float parameter, boolean isStudy, Map<Integer, Float> E
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, OutBack outBack, Matrix rnnMatrix, int[] storeys, int index) throws Exception {
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if (!son.isEmpty()) {
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List<Nerve> nerverList = son.get(0);
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for (Nerve nerve : nerverList) {
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nerve.input(eventId, parameter, isStudy, E, outBack, rnnMatrix, storeys, index, 0);
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}
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} else {
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throw new Exception("this storey is lastIndex");
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}
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}
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protected void clearData(long eventId) {
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}
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protected void sendMyTestMessage(long eventId, Matrix featureMatrix, OutBack outBack, String word) throws Exception {
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}
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protected void sendAppointSoftMax(long eventId, float parameter, Matrix rnnMatrix, OutBack outBack, String myWord) throws Exception {
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}
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protected void sendAppointTestMessage(long eventId, float parameter, Matrix featureMatrix, OutBack outBack, String myWord) throws Exception {
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}
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protected void sendTestMessage(long eventId, float parameter, Matrix featureMatrix, OutBack outBack, String myWord) throws Exception {
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if (!son.isEmpty()) {
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List<Nerve> nerveList = son.get(depth + 1);
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if (nerveList != null) {
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for (Nerve nerve : nerveList) {
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nerve.sendAppointTestMessage(eventId, parameter, featureMatrix, outBack, myWord);
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}
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} else {
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throw new Exception("Insufficient layer:" + depth + 1);
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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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protected void sendRnnTestMessage(long eventId, float parameter, Matrix featureMatrix, OutBack outBack, String myWord) throws Exception {
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if (!rnnOut.isEmpty()) {
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for (Nerve nerve : rnnOut) {
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nerve.sendAppointTestMessage(eventId, parameter, featureMatrix, outBack, myWord);
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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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protected void sendMessage(long eventId, float parameter, boolean isStudy, Map<Integer, Float> E
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, OutBack outBack, Matrix rnnMatrix, int[] storeys, int index, int questionLength) throws Exception {
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List<Nerve> nerveList = null;
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int nextStorey = 0;
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if (storeys == null) {
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nerveList = son.get(0);
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} else {
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nextStorey = getNextStorey(storeys, index);
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if (nextStorey > -1) {//可以继续向前
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nerveList = son.get(nextStorey);
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index++;
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if (nerveList == null) {
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throw new Exception("向前->要查找的层数不存在链接，序列：" + index + "层数:" + nextStorey +
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",当前所在层数:" + depth + ",我的身份:" + name);
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}
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}
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}
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if (nerveList != null) {
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if (creator && !isStudy && nextStorey == startDepth) {
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for (Nerve nerve : nerveList) {
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nerve.sendAppointTestMessage(eventId, parameter, rnnMatrix, outBack, null);
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}
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} else {
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for (Nerve nerve : nerveList) {
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nerve.input(eventId, parameter, isStudy, E, outBack, rnnMatrix, storeys, index, questionLength);
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}
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}
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} else {//发送到输出神经元
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sendRnnMessage(eventId, parameter, isStudy, E, outBack, rnnMatrix, storeys, index);
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}
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}
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private void sendRnnMessage(long eventId, float parameter, boolean isStudy, Map<Integer, Float> E
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, OutBack outBack, Matrix rnnMatrix, int[] storeys, int index) 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, rnnMatrix, storeys, index, 0);
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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, int[] storeys, int index) throws Exception {//反向传播
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if (!father.isEmpty()) {//要先判定是不是可以继续往后传
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List<Nerve> nerveList = null;
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if (storeys == null) {//常规依次回退
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nerveList = father.get(0);
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} else if (index > 0) {//可以继续向后传递
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nerveList = father.get(storeys[index]);
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if (nerveList == null) {
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throw new Exception("向后->要查找的层数不存在链接，序列：" + index + "目标层数:" + storeys[index]
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+ ",当前所在层数:" + depth + ",我的身份:" + name);
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}
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index--;
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}
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if (nerveList != null) {
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for (int i = 0; i < nerveList.size(); i++) {
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nerveList.get(i).backGetMessage(wg.get(i + 1), eventId, fromOutNerve, storeys, index);
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}
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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, Matrix rnnMatrix
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, int[] storeys, int index, int questionLength) throws Exception {//输入参数
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}
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private void backGetMessage(float parameter, long eventId, boolean fromOutNerve, int[] storeys, int index) throws Exception {//反向传播
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backNub++;
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sigmaW = sigmaW + parameter;
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int number;
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if (fromOutNerve) {
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number = outNerveNub;
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} else {
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number = hiddenNerveNub;
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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, storeys, index);//修改阈值
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}
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}
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protected void updatePower(long eventId, int[] storeys, int index) throws Exception {//修改阈值
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float h = gradient * studyPoint;
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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, storeys, index);
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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 = 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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float 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 = 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) throws Exception {//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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featuresList.add(parameter);
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if (featuresList.size() == myUpNumber) {
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allReady = true;
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} else if (featuresList.size() > myUpNumber) {
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throw new Exception("接收参数数量异常");
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}
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return allReady;
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}
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protected void destroyParameter(long eventId) {//销毁参数
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features.remove(eventId);
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}
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protected float calculation(long eventId) {//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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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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return sigma - threshold;
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}
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private void initPower(boolean init) throws Exception {//初始化权重及阈值
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Random random = new Random();
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//设置初始化权重范围收缩系数
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if (name.equals("HiddenNerve")) {//隐层神经元
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myUpNumber = sensoryNerveNub;
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} else if (name.equals("OutNerve")) {//输出神经元
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myUpNumber = hiddenNerveNub;
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} else {//softmax
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myUpNumber = outNerveNub;
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}
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if (myUpNumber > 0) {//输入个数
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float sh = (float) Math.sqrt(myUpNumber);
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for (int i = 1; i < myUpNumber + 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(int depth, List<Nerve> nerveList) {
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son.put(depth, 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(int depth, List<Nerve> nerveList) {
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father.put(depth, nerveList);
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}
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}
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