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package org.dromara.easyai.rnnJumpNerveCenter;
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import org.dromara.easyai.matrixTools.Matrix;
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import org.dromara.easyai.i.ActiveFunction;
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import org.dromara.easyai.rnnJumpNerveEntity.*;
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import java.util.ArrayList;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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/**
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* 神经网络管理工具
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* 创建神经网络
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*
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* @author lidapeng
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* @date 11:05 上午 2019/12/21
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*/
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public class NerveJumpManager {
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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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private final int hiddenDepth;//隐层深度
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private final List<SensoryNerve> sensoryNerves = new ArrayList<>();//感知神经元
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private final List<List<Nerve>> depthNerves = new ArrayList<>();//隐层神经元
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private final List<Nerve> outNerves = new ArrayList<>();//输出神经元
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private final List<Nerve> softMaxList = new ArrayList<>();//softMax层
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private final List<RnnOutNerveBody> rnnOutNerveBodies = new ArrayList<>();//rnn输出层神经元
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private boolean initPower;
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private float studyPoint = 0.1f;//学习率
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private final ActiveFunction activeFunction;
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private final boolean isDynamic;//是否是动态神经网络
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private boolean isRnn = false;//是否为rnn网络
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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 List<NerveCenter> nerveCenterList = new ArrayList<>();//神经中枢集合
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private float powerTh = 0.2f;//权重阈值
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public List<NerveCenter> getNerveCenterList() {
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return nerveCenterList;
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}
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public void setPowerTh(float powerTh) {
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this.powerTh = powerTh;
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}
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private Map<String, Float> conversion(Map<Integer, Float> map) {
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Map<String, Float> cMap = new HashMap<>();
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for (Map.Entry<Integer, Float> entry : map.entrySet()) {
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cMap.put(String.valueOf(entry.getKey()), entry.getValue());
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}
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return cMap;
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}
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private Map<Integer, Float> unConversion(Map<String, Float> map) {
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Map<Integer, Float> cMap = new HashMap<>();
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for (Map.Entry<String, Float> entry : map.entrySet()) {
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cMap.put(Integer.parseInt(entry.getKey()), entry.getValue());
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}
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return cMap;
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}
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private ModelParameter getDymModelParameter() throws Exception {//获取动态神经元参数
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ModelParameter modelParameter = new ModelParameter();
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List<DymNerveStudy> dymNerveStudies = new ArrayList<>();//动态神经元隐层
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DymNerveStudy dymOutNerveStudy = new DymNerveStudy();//动态神经元输出层
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modelParameter.setDymNerveStudies(dymNerveStudies);
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modelParameter.setDymOutNerveStudy(dymOutNerveStudy);
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for (List<Nerve> nerve : depthNerves) {
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Nerve depthNerve = nerve.get(0);//隐层神经元
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DymNerveStudy deepNerveStudy = new DymNerveStudy();//动态神经元输出层
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List<Float> list = deepNerveStudy.getList();
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Matrix matrix = depthNerve.getNerveMatrix();
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insertWList(matrix, list);
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dymNerveStudies.add(deepNerveStudy);
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}
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Nerve outNerve = outNerves.get(0);
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Matrix matrix = outNerve.getNerveMatrix();
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List<Float> list = dymOutNerveStudy.getList();
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insertWList(matrix, list);
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return modelParameter;
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}
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private void insertWList(Matrix matrix, List<Float> list) throws Exception {//
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for (int i = 0; i < matrix.getX(); i++) {
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for (int j = 0; j < matrix.getY(); j++) {
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list.add(matrix.getNumber(i, j));
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}
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}
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}
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public ModelParameter getModelParameter() throws Exception {
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if (isRnn) {
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return getRnnModelParameter();
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} else if (isDynamic) {
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return getDymModelParameter();
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} else {
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return getStaticModelParameter();
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}
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}
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private ModelParameter getRnnModelParameter() {//获取rnn当前模型参数
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ModelParameter modelParameter = new ModelParameter();
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List<List<NerveStudy>> studyDepthNerves = new ArrayList<>();//隐层神经元模型
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List<RnnOutNerveStudy> rnnOutNerveStudies = new ArrayList<>();
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modelParameter.setDepthNerves(studyDepthNerves);
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modelParameter.setRnnOutNerveStudies(rnnOutNerveStudies);
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//隐层神经元
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for (List<Nerve> depthNerve : depthNerves) {
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//创建一层深度的隐层神经元模型
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List<NerveStudy> deepNerve = new ArrayList<>();
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for (Nerve nerve : depthNerve) {
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//遍历某一层深度的所有隐层神经元
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NerveStudy nerveStudy = new NerveStudy();
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nerveStudy.setThreshold(nerve.getThreshold());
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nerveStudy.setDendrites(conversion(nerve.getDendrites()));
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deepNerve.add(nerveStudy);
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}
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studyDepthNerves.add(deepNerve);
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}
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for (RnnOutNerveBody rnnOutNerveBody : rnnOutNerveBodies) {
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List<NerveStudy> nerveStudies = new ArrayList<>();
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RnnOutNerveStudy rnnOutNerveStudy = new RnnOutNerveStudy();
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rnnOutNerveStudies.add(rnnOutNerveStudy);
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rnnOutNerveStudy.setDepth(rnnOutNerveBody.getDepth());
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rnnOutNerveStudy.setNerveStudies(nerveStudies);
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List<Nerve> outNerveList = rnnOutNerveBody.getOutNerves();
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getOutNerveModel(nerveStudies, outNerveList);
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}
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return modelParameter;
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}
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private void getOutNerveModel(List<NerveStudy> nerveStudies, List<Nerve> outNerveList) {
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for (Nerve nerve : outNerveList) {
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NerveStudy nerveStudy = new NerveStudy();
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nerveStudy.setThreshold(nerve.getThreshold());
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nerveStudy.setDendrites(conversion(nerve.getDendrites()));
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nerveStudies.add(nerveStudy);
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}
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}
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private ModelParameter getStaticModelParameter() {//获取当前模型参数
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ModelParameter modelParameter = new ModelParameter();
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List<List<NerveStudy>> studyDepthNerves = new ArrayList<>();//隐层神经元模型
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List<NerveStudy> outStudyNerves = new ArrayList<>();//输出神经元
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//隐层神经元
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for (List<Nerve> depthNerve : depthNerves) {
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//创建一层深度的隐层神经元模型
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List<NerveStudy> deepNerve = new ArrayList<>();
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getOutNerveModel(deepNerve, depthNerve);
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studyDepthNerves.add(deepNerve);
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}
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for (Nerve nerve : outNerves) {
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NerveStudy nerveStudy = new NerveStudy();
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nerveStudy.setThreshold(nerve.getThreshold());
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nerveStudy.setDendrites(conversion(nerve.getDendrites()));
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outStudyNerves.add(nerveStudy);
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}
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modelParameter.setDepthNerves(studyDepthNerves);
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modelParameter.setOutNerves(outStudyNerves);
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return modelParameter;
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}
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public void insertModelParameter(ModelParameter modelParameter) throws Exception {
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if (isRnn) {
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insertRnnModelParameter(modelParameter);
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} else if (isDynamic) {
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insertConvolutionModelParameter(modelParameter);//动态神经元注入
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} else {
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insertBpModelParameter(modelParameter);//全连接层注入参数
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}
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}
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//注入卷积层模型参数
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private void insertConvolutionModelParameter(ModelParameter modelParameter) throws Exception {
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List<DymNerveStudy> dymNerveStudyList = modelParameter.getDymNerveStudies();
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DymNerveStudy dymOutNerveStudy = modelParameter.getDymOutNerveStudy();
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for (int i = 0; i < depthNerves.size(); i++) {
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Nerve depthNerve = depthNerves.get(i).get(0);
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DymNerveStudy dymNerveStudy = dymNerveStudyList.get(i);
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List<Float> list = dymNerveStudy.getList();
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Matrix nerveMatrix = depthNerve.getNerveMatrix();
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insertMatrix(nerveMatrix, list);
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}
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Nerve outNerve = outNerves.get(0);
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Matrix outNerveMatrix = outNerve.getNerveMatrix();
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List<Float> list = dymOutNerveStudy.getList();
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insertMatrix(outNerveMatrix, list);
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}
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private void insertMatrix(Matrix matrix, List<Float> list) throws Exception {
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for (int i = 0; i < list.size(); i++) {
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matrix.setNub(i, 0, list.get(i));
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}
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}
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private void insertRnnModelParameter(ModelParameter modelParameter) throws Exception {
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List<List<NerveStudy>> depthStudyNerves = modelParameter.getDepthNerves();//隐层神经元
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List<RnnOutNerveStudy> rnnOutNerveStudies = modelParameter.getRnnOutNerveStudies();
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//隐层神经元参数注入
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depthNervesModel(depthStudyNerves);
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for (RnnOutNerveStudy rnnOutNerveStudy : rnnOutNerveStudies) {
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RnnOutNerveBody rnnOutNerveBody = getRnnOutNerveBody(rnnOutNerveStudy.getDepth());
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List<NerveStudy> outStudyNerves = rnnOutNerveStudy.getNerveStudies();
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List<Nerve> outNerveBody = rnnOutNerveBody.getOutNerves();
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//输出神经元参数注入
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outNerveModel(outStudyNerves, outNerveBody);
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}
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}
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private void outNerveModel(List<NerveStudy> outStudyNerves, List<Nerve> outNerveBody) {
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for (int i = 0; i < outNerveBody.size(); i++) {
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Nerve outNerve = outNerveBody.get(i);
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NerveStudy nerveStudy = outStudyNerves.get(i);
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outNerve.setThreshold(nerveStudy.getThreshold());
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Map<Integer, Float> dendrites = outNerve.getDendrites();
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Map<Integer, Float> studyDendrites = unConversion(nerveStudy.getDendrites());
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for (Map.Entry<Integer, Float> outEntry : dendrites.entrySet()) {
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int key = outEntry.getKey();
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dendrites.put(key, studyDendrites.get(key));
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}
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}
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}
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private void depthNervesModel(List<List<NerveStudy>> depthStudyNerves) {
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for (int i = 0; i < depthNerves.size(); i++) {
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List<NerveStudy> depth = depthStudyNerves.get(i);//对应的学习结果
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List<Nerve> depthNerve = depthNerves.get(i);//深度隐层神经元
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for (int j = 0; j < depthNerve.size(); j++) {//遍历当前深度神经元
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Nerve nerve = depthNerve.get(j);
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NerveStudy nerveStudy = depth.get(j);
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//学习结果
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Map<Integer, Float> studyDendrites = unConversion(nerveStudy.getDendrites());
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//神经元参数注入
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Map<Integer, Float> dendrites = nerve.getDendrites();
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nerve.setThreshold(nerveStudy.getThreshold());//注入隐层阈值
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for (Map.Entry<Integer, Float> entry : dendrites.entrySet()) {
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int key = entry.getKey();
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dendrites.put(key, studyDendrites.get(key));//注入隐层权重
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}
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}
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}
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}
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private RnnOutNerveBody getRnnOutNerveBody(int depth) {
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RnnOutNerveBody myRnnOutNerveBody = null;
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for (RnnOutNerveBody rnnOutNerveBody : rnnOutNerveBodies) {
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if (rnnOutNerveBody.getDepth() == depth) {
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myRnnOutNerveBody = rnnOutNerveBody;
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break;
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}
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}
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return myRnnOutNerveBody;
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}
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//注入全连接模型参数
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private void insertBpModelParameter(ModelParameter modelParameter) {
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List<List<NerveStudy>> depthStudyNerves = modelParameter.getDepthNerves();//隐层神经元
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List<NerveStudy> outStudyNerves = modelParameter.getOutNerves();//输出神经元
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//隐层神经元参数注入
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depthNervesModel(depthStudyNerves);
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//输出神经元参数注入
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outNerveModel(outStudyNerves, outNerves);
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}
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/**
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* 初始化神经元参数
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*
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* @param sensoryNerveNub 输入神经元个数
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* @param hiddenNerveNub 隐层神经元个数
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* @param outNerveNub 输出神经元个数
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* @param hiddenDepth 隐层深度
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* @param activeFunction 激活函数
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* @param isDynamic 是否是动态神经元
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* @param rzType 正则函数
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* @param lParam 正则系数
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* @throws Exception 如果参数错误则抛异常
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*/
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public NerveJumpManager(int sensoryNerveNub, int hiddenNerveNub, int outNerveNub
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, int hiddenDepth, ActiveFunction activeFunction, boolean isDynamic,
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float studyPoint, int rzType, float lParam) throws Exception {
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if (sensoryNerveNub > 0 && hiddenNerveNub > 0 && outNerveNub > 0 && hiddenDepth > 0 && activeFunction != null) {
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this.hiddenNerveNub = hiddenNerveNub;
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this.sensoryNerveNub = sensoryNerveNub;
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this.outNerveNub = outNerveNub;
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this.hiddenDepth = hiddenDepth;
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this.activeFunction = activeFunction;
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this.isDynamic = isDynamic;
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this.rzType = rzType;
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this.lParam = lParam;
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if (studyPoint > 0 && studyPoint < 1) {
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this.studyPoint = studyPoint;
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}
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} else {
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throw new Exception("param is null");
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}
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}
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public List<SensoryNerve> getSensoryNerves() {//获取感知神经元集合
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return sensoryNerves;
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}
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/**
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* 初始化
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*
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* @param initPower 是否是第一次注入
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* @param isShowLog 是否打印学习参数
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* @param isSoftMax 最后一层是否用softMax激活
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* @param step 卷积步长
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* @param kernLen 卷积核长
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*/
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public void init(boolean initPower, boolean isShowLog, boolean isSoftMax, int step, int kernLen) throws Exception {//进行神经网络的初始化构建
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this.initPower = initPower;
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initDepthNerve(false, 0);//初始化深度隐层神经元
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List<Nerve> nerveList = depthNerves.get(0);//第一层隐层神经元
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//最后一层隐层神经元啊
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List<Nerve> lastNerveList = depthNerves.get(depthNerves.size() - 1);
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List<OutNerve> outNerveList = new ArrayList<>();
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//初始化输出神经元
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for (int i = 1; i < outNerveNub + 1; i++) {
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OutNerve outNerve = new OutNerve(i, studyPoint, initPower,
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activeFunction, isShowLog, rzType, lParam, isSoftMax, sensoryNerveNub, hiddenNerveNub, outNerveNub, hiddenDepth);
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//输出层神经元连接最后一层隐层神经元
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outNerve.connectFather(0, lastNerveList);
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outNerves.add(outNerve);
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outNerveList.add(outNerve);
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}
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//生成softMax层
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if (isSoftMax) {//增加softMax层
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SoftMax softMax = new SoftMax(outNerveList, isShowLog, sensoryNerveNub, hiddenNerveNub, outNerveNub, hiddenDepth);
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softMaxList.add(softMax);
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for (Nerve nerve : outNerves) {
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nerve.connect(0, softMaxList);
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}
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}
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//最后一层隐层神经元 与输出神经元进行连接
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for (Nerve nerve : lastNerveList) {
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nerve.connect(0, outNerves);
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}
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//初始化感知神经元
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for (int i = 1; i < sensoryNerveNub + 1; i++) {
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SensoryNerve sensoryNerve = new SensoryNerve(i, hiddenDepth);
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//感知神经元与第一层隐层神经元进行连接
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sensoryNerve.connect(0, nerveList);
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sensoryNerves.add(sensoryNerve);
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}
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}
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private void createRnnOutNerve(boolean initPower, boolean isShowLog, List<Nerve> nerveList, int depth
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, boolean toSoftMax) throws Exception {
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RnnOutNerveBody rnnOutNerveBody = new RnnOutNerveBody();
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List<Nerve> mySoftMaxList = new ArrayList<>();
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List<Nerve> rnnOutNerves = new ArrayList<>();
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List<OutNerve> layOutNerves = new ArrayList<>();
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rnnOutNerveBody.setDepth(depth);
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rnnOutNerveBody.setOutNerves(rnnOutNerves);
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NerveCenter nerveCenter = nerveCenterList.get(depth);
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for (int i = 1; i < outNerveNub + 1; i++) {
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OutNerve outNerve = new OutNerve(i, studyPoint, initPower,
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activeFunction, isShowLog, rzType, lParam, toSoftMax, sensoryNerveNub, hiddenNerveNub, outNerveNub, hiddenDepth);
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outNerve.connectFather(depth, nerveList);//每一层的输出神经元 链接每一层的隐层神经元
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rnnOutNerves.add(outNerve);
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layOutNerves.add(outNerve);
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}
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if (toSoftMax) {
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SoftMax softMax = new SoftMax(layOutNerves, isShowLog, sensoryNerveNub, hiddenNerveNub, outNerveNub, hiddenDepth);
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softMax.setNerveCenter(nerveCenter);
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mySoftMaxList.add(softMax);
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for (Nerve nerve : rnnOutNerves) {
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nerve.connect(0, mySoftMaxList);
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}
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}
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for (Nerve nerve : nerveList) {
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nerve.connectOut(rnnOutNerves);
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}
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rnnOutNerveBodies.add(rnnOutNerveBody);
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}
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public void initRnn(boolean initPower, boolean isShowLog, boolean toSoftMax, boolean creator, int startDepth) throws Exception {
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isRnn = true;
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this.initPower = initPower;
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initDepthNerve(creator, startDepth);//初始化深度隐层神经元
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for (int i = 0; i < depthNerves.size(); i++) {
|
||||
createRnnOutNerve(initPower, isShowLog, depthNerves.get(i), i + 1, toSoftMax);
|
||||
}
|
||||
//初始化感知神经元
|
||||
for (int i = 1; i < sensoryNerveNub + 1; i++) {
|
||||
SensoryNerve sensoryNerve = new SensoryNerve(i, hiddenDepth);
|
||||
for (int j = 0; j < hiddenDepth; j++) {
|
||||
List<Nerve> hiddenNerveList = depthNerves.get(j);//当前遍历隐层神经元
|
||||
sensoryNerve.connect(j + 1, hiddenNerveList);
|
||||
}
|
||||
sensoryNerves.add(sensoryNerve);
|
||||
}
|
||||
}
|
||||
|
||||
private void initDepthNerve(boolean creator, int startDepth) throws Exception {//初始化隐层神经元1
|
||||
if (isRnn) {
|
||||
NerveCenter nerveCenter = new NerveCenter(0, null, powerTh, false);
|
||||
nerveCenterList.add(nerveCenter);
|
||||
}
|
||||
for (int i = 0; i < hiddenDepth; i++) {//遍历深度
|
||||
List<Nerve> hiddenNerveList = new ArrayList<>();
|
||||
float studyPoint = this.studyPoint;
|
||||
if (studyPoint <= 0 || studyPoint > 1) {
|
||||
throw new Exception("studyPoint Values range from 0 to 1");
|
||||
}
|
||||
if (isRnn) {
|
||||
boolean isFinish = i == hiddenDepth - 1;
|
||||
NerveCenter nerveCenter = new NerveCenter(i + 1, hiddenNerveList, powerTh, isFinish);
|
||||
nerveCenterList.add(nerveCenter);
|
||||
}
|
||||
for (int j = 1; j < hiddenNerveNub + 1; j++) {//遍历同级
|
||||
HiddenNerve hiddenNerve = new HiddenNerve(j, i + 1, studyPoint, initPower, activeFunction
|
||||
, rzType, lParam, sensoryNerveNub, hiddenNerveNub, outNerveNub,
|
||||
hiddenDepth, creator, startDepth);
|
||||
hiddenNerveList.add(hiddenNerve);
|
||||
}
|
||||
depthNerves.add(hiddenNerveList);
|
||||
}
|
||||
if (isRnn) {
|
||||
initRnnHiddenNerve();
|
||||
} else {
|
||||
initHiddenNerve();
|
||||
}
|
||||
}
|
||||
|
||||
private void initHiddenNerve() {
|
||||
for (int i = 0; i < hiddenDepth - 1; i++) {
|
||||
List<Nerve> hiddenNerveList = depthNerves.get(i);//当前遍历隐层神经元
|
||||
List<Nerve> nextHiddenNerveList = depthNerves.get(i + 1);
|
||||
for (Nerve nerve : hiddenNerveList) {
|
||||
nerve.connect(0, nextHiddenNerveList);
|
||||
}
|
||||
for (Nerve nerve : nextHiddenNerveList) {
|
||||
nerve.connectFather(0, hiddenNerveList);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private void initRnnHiddenNerve() {//初始化隐层神经元2
|
||||
for (int i = 0; i < hiddenDepth; i++) {//遍历深度
|
||||
List<Nerve> hiddenNerveList = depthNerves.get(i);//当前遍历隐层神经元
|
||||
if (i < hiddenDepth - 1) {//向前链接
|
||||
for (int j = i + 1; j < hiddenDepth; j++) {
|
||||
List<Nerve> nextHiddenNerveList = depthNerves.get(j);
|
||||
for (Nerve hiddenNerve : hiddenNerveList) {
|
||||
hiddenNerve.connect(j + 1, nextHiddenNerveList);
|
||||
}
|
||||
}
|
||||
}
|
||||
if (i > 0) {//向后链接
|
||||
for (int t = i - 1; t >= 0; t--) {
|
||||
List<Nerve> nextHiddenNerveList = depthNerves.get(t);
|
||||
for (Nerve hiddenNerve : hiddenNerveList) {
|
||||
hiddenNerve.connectFather(t + 1, nextHiddenNerveList);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user