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458
src/main/java/org/dromara/easyai/nerveEntity/Nerve.java
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458
src/main/java/org/dromara/easyai/nerveEntity/Nerve.java
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package org.dromara.easyai.nerveEntity;
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import org.dromara.easyai.conv.ConvCount;
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import org.dromara.easyai.conv.ConvResult;
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import org.dromara.easyai.conv.DymStudy;
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import org.dromara.easyai.entity.ThreeChannelMatrix;
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import org.dromara.easyai.i.CustomEncoding;
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import org.dromara.easyai.matrixTools.Matrix;
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import org.dromara.easyai.matrixTools.MatrixList;
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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 org.dromara.easyai.rnnJumpNerveCenter.CustomManager;
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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 extends ConvCount {
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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> father = new ArrayList<>();//树突上一层的连接神经元
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private Nerve sonOnly;
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private Nerve fatherOnly;
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protected final float gaMa;//自适应学习率衰减系数
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protected Map<Integer, Float> dendrites = new HashMap<>();//上一层权重(需要取出)
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private final Map<Integer, Float> dymStudyRate = new HashMap<>();//动态学习率
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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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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 Map<Long, List<Float>> features = new HashMap<>();//上一层神经元输入的数值
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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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protected List<Matrix> sigmaMatrix;//对上层回传误差进行求和
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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 int kernLen;//核长
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protected final int depth;//所处深度
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protected final int matrixX;//卷积输出行数列数
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protected final int matrixY;//卷积输出矩阵列数
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private final MatrixOperation matrixOperation;
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protected final int channelNo;//通道数
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private final ConvParameter convParameter = new ConvParameter();//内存中卷积层模型及临时数据
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protected final float oneConvRate;
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private final boolean norm;//是否进行1v1卷积升降维
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private final CustomEncoding customEncoding;//自定义编码模块
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private final float gMaxTh;
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private final DymStudy dymStudy;
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private final boolean auto;
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private final float GRate;//梯度衰减
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public Map<Integer, Float> getDendrites() {
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return dendrites;
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}
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public ConvParameter getConvParameter() {
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return convParameter;
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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
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, boolean isDynamic, int rzType, float lParam, int kernLen, int depth
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, int matrixX, int matrixY, int coreNumber, int channelNo, float onConvRate, boolean norm, CustomEncoding customEncoding
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, float gaMa, float gMaxTh, boolean auto, float GRate) throws Exception {//该神经元在同层神经元中的编号
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if (auto) {
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if (gaMa <= 0 || gaMa >= 1) {
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throw new IllegalArgumentException("gaMa 取值范围是(0,1)，当前值:" + gaMa);
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}
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if (gMaxTh <= 0) {
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throw new IllegalArgumentException("gMaxTh 必须比0大,当前值:" + gMaxTh);
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}
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}
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matrixOperation = new MatrixOperation(coreNumber);
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dymStudy = new DymStudy(gaMa, gMaxTh, auto);
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this.GRate = GRate;
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this.gaMa = gaMa;
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this.auto = auto;
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this.gMaxTh = gMaxTh;
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this.matrixX = matrixX;
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this.customEncoding = customEncoding;
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this.norm = norm;
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this.matrixY = matrixY;
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this.channelNo = channelNo;
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this.id = id;
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this.depth = depth;
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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.kernLen = kernLen;
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this.oneConvRate = onConvRate;
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initPower(init, isDynamic);//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) 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);
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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 List<Matrix> conv(List<Matrix> matrix) throws Exception {//一次正向卷积，下取样
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return downConvAndPooling(matrix, convParameter, channelNo, activeFunction, kernLen, true, -1);
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}
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protected void demRedByMatrixList(long eventId, List<Matrix> matrixList, boolean study,
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Map<Integer, Float> E, OutBack outBack, boolean needMatrix) throws Exception {
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if (study) {//训练临时保存
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convParameter.setFeatureMatrixList(matrixList);
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}
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List<Matrix> feature;
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if (norm) {
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feature = manyOneConv(matrixList, convParameter.getOneConvPower());//降维后的特征矩阵
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} else {
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if (matrixList.size() != 3) {
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throw new Exception("不进行维度调节，输入的特征矩阵通道数必须为3");
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}
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feature = matrixList;
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}
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List<Matrix> convMatrix = conv(feature);
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sendMatrix(eventId, convMatrix, study, E, outBack, needMatrix);
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}
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public void sendMatrixList(long eventId, List<Float> parameter, boolean isStudy,
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Map<Integer, Float> E, OutBack outBack) throws Exception {
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if (!son.isEmpty()) {
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for (Nerve nerve : son) {
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nerve.inputMatrixFeature(eventId, parameter, isStudy, E, outBack);
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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 sendMatrix(long eventId, List<Matrix> parameter, boolean isStudy,
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Map<Integer, Float> E, OutBack outBack, boolean needMatrix) throws Exception {
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if (sonOnly != null) {
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sonOnly.inputMatrix(eventId, parameter, isStudy, E, outBack, needMatrix);
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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 sendThreeChannelMatrix(long eventId, ThreeChannelMatrix parameter, boolean isStudy,
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Map<Integer, Float> E, OutBack outBack, boolean needMatrix) throws Exception {
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if (sonOnly != null) {
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sonOnly.inputThreeChannelMatrix(eventId, parameter, isStudy, E, outBack, needMatrix);
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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 sendListMatrix(long eventId, List<Matrix> parameter, boolean isStudy,
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Map<Integer, Float> E, OutBack outBack, boolean needMatrix) throws Exception {
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if (sonOnly != null) {
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sonOnly.demRedByMatrixList(eventId, parameter, isStudy, E, outBack, needMatrix);
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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) 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);
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}
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} else if (fatherOnly != null && depth == 1) {//反矩阵误差
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List<Matrix> errorMatrixList = new ArrayList<>();
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int size = this.matrixX * this.matrixY;
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int featureSize = wg.size() / size;
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for (int i = 0; i < featureSize; ++i) {
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List<Float> list = new ArrayList<>();
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int startIndex = size * i;
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int endIndex = startIndex + size;
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for (int j = startIndex; j < endIndex; j++) {
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list.add(this.wg.get(j + 1));
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}
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Matrix errorMatrix = this.matrixOperation.ListToMatrix(list, this.matrixX, this.matrixY);
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errorMatrixList.add(errorMatrix);
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}
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fatherOnly.backMatrix(errorMatrixList);
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} else if (customEncoding != null && depth == 1) {//最后一层返回给第一层
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customEncoding.backError(wg, id);
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}
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}
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private void backMatrixMessage(List<Matrix> g) throws Exception {//反向传播矩阵
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if (fatherOnly != null) {
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fatherOnly.backMatrix(g);
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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) throws Exception {//输入参数
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}
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protected void inputMatrixFeature(long eventId, List<Float> parameters, boolean isStudy
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, Map<Integer, Float> E, OutBack imageBack) throws Exception {//卷积层向网络发送参数
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}
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protected void inputMatrix(long eventId, List<Matrix> matrix, boolean isKernelStudy, Map<Integer, Float> E,
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OutBack outBack, boolean needMatrix) throws Exception {//输入动态矩阵
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}
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protected void inputThreeChannelMatrix(long eventId, ThreeChannelMatrix picture, boolean isKernelStudy, Map<Integer, Float> E,
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OutBack outBack, boolean needMatrix) throws Exception {//输入动态矩阵
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}
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private void backGetMessage(float parameter, long eventId) throws Exception {//反向传播
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backNub++;
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sigmaW = sigmaW + parameter;
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if (backNub == downNub) {//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 backMatrix(List<Matrix> t) throws Exception {//回传梯度
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backNub++;
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if (sigmaMatrix == null) {
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sigmaMatrix = t;
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} else {
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sigmaMatrix = matrixOperation.addMatrixList(t, sigmaMatrix);
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}
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if (backNub == downNub) {
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backNub = 0;
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List<Matrix> errorMatrix = backDownPoolingByList(sigmaMatrix, convParameter.getOutX(), convParameter.getOutY());//池化误差返回
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matrixOperation.mathMulByList(errorMatrix, GRate);
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List<Matrix> myErrorMatrix = backAllDownConv(convParameter, errorMatrix, studyPoint, activeFunction, channelNo, kernLen,
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gaMa, gMaxTh, auto);
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sigmaMatrix = null;
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if (depth == 1) {//1*1 卷积调整
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if (norm) {
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backOneConvByList(myErrorMatrix, convParameter.getFeatureMatrixList(), convParameter.getOneConvPower(), oneConvRate
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, convParameter.getOneDymStudyRateList(), gaMa, gMaxTh, auto);
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}
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} else {
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//将梯度继续回传
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backMatrixMessage(myErrorMatrix);
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}
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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 thError = dymStudy.getOneValueError(studyPoint, gradient, convParameter);
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threshold = threshold - thError;
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updateW(gradient, eventId);
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sigmaW = 0;//求和结果归零
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backSendMessage(eventId);
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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 + 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 error = dymStudy.getNerveStudyError(dymStudyRate, key, h, studyPoint);
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float wp = bn * error;
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float dm = w * h;
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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 void insertParameters(long eventId, List<Float> parameters) {//添加参数
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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.addAll(parameters);
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}
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protected boolean insertParameter(long eventId, float parameter) {//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() >= 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 calculation(long eventId) throws Exception {//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 (dendrites.size() != featuresList.size()) {
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throw new Exception("权重数量:" + dendrites.size() + ",特征数量:" + featuresList.size());
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}
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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);
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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, boolean isDynamic) throws Exception {//初始化权重及阈值
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Random random = new Random();
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if (!isDynamic) {//静态神经元
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//设置初始化权重范围收缩系数
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if (upNub > 0) {//输入个数
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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() / (float) Math.sqrt(upNub);
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}
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dendrites.put(i, nub);//random.nextFloat()
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dymStudyRate.put(i, 0f);
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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() / (float) Math.sqrt(upNub);
|
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}
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threshold = nub;
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}
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} else {//动态神经元
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initMatrixPower(random);
|
||||
}
|
||||
}
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||||
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||||
private void initMatrixPower(Random random) throws Exception {
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int nerveNub = kernLen * kernLen;
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||||
List<Matrix> nerveMatrixList = convParameter.getNerveMatrixList();//一层当中所有的深度卷积核
|
||||
List<Matrix> dymStudyRateList = convParameter.getDymStudyRateList();
|
||||
List<ConvSize> convSizeList = convParameter.getConvSizeList();
|
||||
List<List<Float>> onePowers = new ArrayList<>();//1*1卷积核
|
||||
List<List<Float>> oneDymStudyRateList = new ArrayList<>();
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||||
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
|
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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<Float> oneConvPowerList = new ArrayList<>();
|
||||
List<Float> 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<Nerve> 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<Nerve> nerveList) {
|
||||
father.addAll(nerveList);//连接上一层
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user