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src/main/java/org/dromara/easyai/nerveCenter/NerveManager.java
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464
src/main/java/org/dromara/easyai/nerveCenter/NerveManager.java
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package org.dromara.easyai.nerveCenter;
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import org.dromara.easyai.conv.ConvCount;
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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.i.ActiveFunction;
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import org.dromara.easyai.nerveEntity.*;
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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 NerveManager extends ConvCount {
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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 SensoryNerve convInput;//卷积网络输入神经元
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private final List<List<Nerve>> depthNerves = new ArrayList<>();//隐层神经元
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private List<Nerve> convDepthNerves = new ArrayList<>();//卷积隐层神经元
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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 boolean initPower;
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private float studyPoint = 0.001f;//学习率
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private float convStudyPoint = 0.001f;//卷积学习率
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private float oneConvRate = 0.001f;
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private final 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 coreNumber;
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private final float gaMa;//自适应学习率
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private final float gMaxTh;//梯度裁剪阈值
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private final boolean auto;
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public SensoryNerve getConvInput() {
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return convInput;
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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<ConvDymNerveStudy> convStudies = new ArrayList<>();
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modelParameter.setDymNerveStudies(convStudies);
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for (Nerve convDepthNerve : convDepthNerves) {
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ConvParameter convParameter = convDepthNerve.getConvParameter();
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List<Matrix> nerveMatrixList = convParameter.getNerveMatrixList();//权重矩阵
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ConvDymNerveStudy convDymNerveStudy = new ConvDymNerveStudy();
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List<List<Float>> oneConvList = convParameter.getOneConvPower();
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List<DymNerveStudy> dymNerveStudies = new ArrayList<>();//一个卷积层的所有权重参数
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convDymNerveStudy.setOneConvPower(oneConvList);
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convDymNerveStudy.setDymNerveStudyList(dymNerveStudies);
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for (Matrix nerveMatrix : nerveMatrixList) {
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DymNerveStudy deepNerveStudy = new DymNerveStudy();//动态神经元隐层
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List<Float> list = deepNerveStudy.getList();
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insertWList(nerveMatrix, list);
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dymNerveStudies.add(deepNerveStudy);
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}
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convStudies.add(convDymNerveStudy);
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}
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getStaticModelParameter(modelParameter);
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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 getConvModel() throws Exception {
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return getDymModelParameter();
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}
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public ModelParameter getDnnModel() throws Exception {
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ModelParameter modelParameter = new ModelParameter();
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getStaticModelParameter(modelParameter);
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return modelParameter;
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}
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private void getStaticModelParameter(ModelParameter modelParameter) {//获取当前模型参数
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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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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 (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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}
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public void insertConvModel(ModelParameter modelParameter) throws Exception {
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insertConvolutionModelParameter(modelParameter);//动态神经元注入
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}
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public void insertDnnModel(ModelParameter modelParameter) {
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insertBpModelParameter(modelParameter);//全连接层注入参数
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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<ConvDymNerveStudy> allDymNerveStudyList = modelParameter.getDymNerveStudies();
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for (int t = 0; t < allDymNerveStudyList.size(); t++) {
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ConvParameter convParameter = convDepthNerves.get(t).getConvParameter();
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List<Matrix> nerveMatrixList = convParameter.getNerveMatrixList();
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ConvDymNerveStudy convDymNerveStudy = allDymNerveStudyList.get(t);
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List<List<Float>> oneConvPower = convDymNerveStudy.getOneConvPower();
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if (oneConvPower != null && !oneConvPower.isEmpty()) {
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convParameter.setOneConvPower(oneConvPower);
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}
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List<DymNerveStudy> dymNerveStudyList = convDymNerveStudy.getDymNerveStudyList();
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if (dymNerveStudyList.size() != nerveMatrixList.size()) {
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throw new Exception("卷积层数量参数与模型不匹配");
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}
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for (int i = 0; i < dymNerveStudyList.size(); i++) {
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List<Float> list = dymNerveStudyList.get(i).getList();
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Matrix nerveMatrix = nerveMatrixList.get(i);
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insertMatrix(nerveMatrix, list);
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}
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}
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insertBpModelParameter(modelParameter);//全连接层注入参数
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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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//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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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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//输出神经元参数注入
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for (int i = 0; i < outNerves.size(); i++) {
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Nerve outNerve = outNerves.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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/**
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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 studyPoint 线性分类器学习率
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* @param rzType 正则函数
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* @param lParam 正则系数
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* @param coreNumber 并行计算核心数
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* @param gaMa 自适应学习率衰减系数
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* @param gMaxTh 梯度裁剪阈值
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* @param auTo 是否使用自适应学习率
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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 NerveManager(int sensoryNerveNub, int hiddenNerveNub, int outNerveNub
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, int hiddenDepth, ActiveFunction activeFunction, float studyPoint, int rzType, float lParam
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, int coreNumber, float gaMa, float gMaxTh, boolean auTo) throws Exception {
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if (sensoryNerveNub > 0 && hiddenNerveNub > 0 && outNerveNub > 0 && hiddenDepth > 0 && activeFunction != null) {
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this.coreNumber = coreNumber;
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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.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.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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private List<Nerve> initConDepthNerve(int kernLen, int conHiddenDepth, ActiveFunction convFunction, int channelNo, boolean norm, float GRate) throws Exception {//初始化隐层神经元1
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List<Nerve> depthNerves = new ArrayList<>();
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for (int i = 0; i < conHiddenDepth; i++) {//遍历深度
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float studyPoint = this.convStudyPoint;
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if (studyPoint <= 0 || studyPoint > 1) {
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throw new Exception("studyPoint Values range from 0 to 1");
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}
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int downNub = 1;
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boolean isConvFinish = false;
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if (i == conHiddenDepth - 1) {//卷积层最后一层
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downNub = hiddenNerveNub;
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isConvFinish = true;
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}
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HiddenNerve hiddenNerve = new HiddenNerve(1, i + 1, 1, downNub, studyPoint, initPower, convFunction, true
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, rzType, lParam, kernLen, 0, 0, isConvFinish, coreNumber, channelNo, oneConvRate, norm,
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null, gaMa, gMaxTh, auto, GRate);
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depthNerves.add(hiddenNerve);
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}
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for (int i = 0; i < conHiddenDepth - 1; i++) {//遍历深度
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Nerve hiddenNerve = depthNerves.get(i);//当前遍历隐层神经元
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Nerve nextHiddenNerve = depthNerves.get(i + 1);//当前遍历的下一层神经元
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hiddenNerve.connectSonOnly(nextHiddenNerve);
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nextHiddenNerve.connectFatherOnly(hiddenNerve);
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}
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return depthNerves;
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}
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private int getNerveNub(int deep, int size, int kernLen) {
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int x = size;
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int step = 1;
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for (int i = 0; i < deep; i++) {
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x = (x - (kernLen - step)) / step;
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x = x / 2 + x % 2;
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}
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return x;
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}
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/**
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* 初始化卷积层神经网络
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*
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* @param channelNo 通道数，当该数值为1 则采用多通道降维模式 推荐值1
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* @param kernLen 卷积核大小 建议为3
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* @param xSize 检测窗口行高
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* @param ySize 检测窗口行宽
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* @param convStudyPoint 卷积层学习率
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* @param convFunction 卷积层激活函数
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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 minFeatureValue 卷积层最小特征数量的开方 取值范围 [1,50]
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* @param norm 是否进行维度调节，true 进行调节， false不进行维度调节
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* @param oneConvRate 降维层学习率
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* @param GRate 每层的梯度衰减阈值
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*/
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public void initImageNet(int channelNo, int kernLen, int xSize, int ySize, boolean isSoftMax, boolean isShowLog,
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float convStudyPoint, ActiveFunction convFunction, int minFeatureValue, float oneConvRate
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, boolean norm, float GRate) throws Exception {
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this.initPower = true;
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this.oneConvRate = oneConvRate;
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if (minFeatureValue < 1 || minFeatureValue > 50) {
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throw new Exception("minFeatureValue 取值范围是[1,50]");
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}
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if (channelNo < 1) {
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throw new Exception("通道数不能小于1");
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}
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if (!norm) {//如果不进行维度调节，通道数必须为3
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channelNo = 3;
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}
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this.convStudyPoint = convStudyPoint;
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int deep = getConvMyDep(xSize, ySize, kernLen, minFeatureValue);//卷积层深度
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if (deep < 2) {
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throw new Exception("minFeatureValue 设置过大");
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}
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List<Nerve> myDepthNerves = initConDepthNerve(kernLen, deep, convFunction, channelNo, norm, GRate);//初始化卷积层隐层
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Nerve convFirstNerve = myDepthNerves.get(0);//卷积第一层隐层神经元
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Nerve convLastNerve = myDepthNerves.get(myDepthNerves.size() - 1);//卷积最后一层隐层神经元
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convDepthNerves = myDepthNerves;
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convInput = new SensoryNerve(1, 0, channelNo);//输入神经元
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//感知神经元与卷积第一层隐层神经元进行连接
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convInput.connectSonOnly(convFirstNerve);
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initDepthNerve(kernLen, getNerveNub(deep, xSize, kernLen), getNerveNub(deep, ySize, kernLen), channelNo, null);//初始化深度隐层神经元 depthNerves
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List<Nerve> firstNerves = depthNerves.get(0);//线性层第一层隐层神经元
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List<Nerve> lastNerveList = depthNerves.get(depthNerves.size() - 1);//线性层最后一层隐层神经元
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convLastNerve.connect(firstNerves);//卷积最后一层链接线性层第一层
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for (Nerve nerve : firstNerves) {//线性层第一层链接卷积层最后一层
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nerve.connectFatherOnly(convLastNerve);
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}
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List<OutNerve> myOutNerveList = 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, hiddenNerveNub, 0, studyPoint, initPower,
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activeFunction, false, isShowLog, rzType, lParam, isSoftMax, 0
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, coreNumber, gaMa, gMaxTh, auto, 1);
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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(lastNerveList);
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outNerves.add(outNerve);
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myOutNerveList.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(outNerveNub, false, myOutNerveList, isShowLog, coreNumber);
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softMaxList.add(softMax);
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for (Nerve nerve : outNerves) {
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nerve.connect(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(outNerves);
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}
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||||
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||||
}
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||||
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||||
/**
|
||||
* 初始化
|
||||
*
|
||||
* @param initPower 是否是第一次注入
|
||||
* @param isShowLog 是否打印学习参数
|
||||
* @param isSoftMax 最后一层是否用softMax激活
|
||||
*/
|
||||
public void init(boolean initPower, boolean isShowLog, boolean isSoftMax, CustomEncoding customEncoding) throws Exception {//进行神经网络的初始化构建
|
||||
this.initPower = initPower;
|
||||
initDepthNerve(0, 0, 0, 0, customEncoding);//初始化深度隐层神经元
|
||||
List<Nerve> nerveList = depthNerves.get(0);//第一层隐层神经元
|
||||
//最后一层隐层神经元啊
|
||||
List<Nerve> lastNerveList = depthNerves.get(depthNerves.size() - 1);
|
||||
List<OutNerve> myOutNerveList = new ArrayList<>();
|
||||
//初始化输出神经元
|
||||
for (int i = 1; i < outNerveNub + 1; i++) {
|
||||
OutNerve outNerve = new OutNerve(i, hiddenNerveNub, 0, studyPoint, initPower,
|
||||
activeFunction, false, isShowLog, rzType, lParam, isSoftMax, 0
|
||||
, coreNumber, gaMa, gMaxTh, auto, 1);
|
||||
//输出层神经元连接最后一层隐层神经元
|
||||
outNerve.connectFather(lastNerveList);
|
||||
outNerves.add(outNerve);
|
||||
myOutNerveList.add(outNerve);
|
||||
}
|
||||
//生成softMax层
|
||||
if (isSoftMax) {//增加softMax层
|
||||
SoftMax softMax = new SoftMax(outNerveNub, false, myOutNerveList, isShowLog, coreNumber);
|
||||
softMaxList.add(softMax);
|
||||
for (Nerve nerve : outNerves) {
|
||||
nerve.connect(softMaxList);
|
||||
}
|
||||
}
|
||||
//最后一层隐层神经元 与输出神经元进行连接
|
||||
for (Nerve nerve : lastNerveList) {
|
||||
nerve.connect(outNerves);
|
||||
}
|
||||
|
||||
//初始化感知神经元
|
||||
for (int i = 1; i < sensoryNerveNub + 1; i++) {
|
||||
SensoryNerve sensoryNerve = new SensoryNerve(i, 0, 0);
|
||||
//感知神经元与第一层隐层神经元进行连接
|
||||
sensoryNerve.connect(nerveList);
|
||||
sensoryNerves.add(sensoryNerve);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
private void initDepthNerve(int kernLen, int matrixX, int matrixY, int channelNo, CustomEncoding customEncoding) throws Exception {//初始化隐层神经元1
|
||||
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");
|
||||
}
|
||||
CustomEncoding myCustomEncoding = null;
|
||||
if (i == 0) {
|
||||
myCustomEncoding = customEncoding;
|
||||
}
|
||||
for (int j = 1; j < hiddenNerveNub + 1; j++) {//遍历同级
|
||||
int upNub;
|
||||
int downNub;
|
||||
int myMatrixX = 0;
|
||||
int myMatrixY = 0;
|
||||
if (i == 0) {
|
||||
myMatrixX = matrixX;
|
||||
myMatrixY = matrixY;
|
||||
if (matrixX > 0 && matrixY > 0) {
|
||||
upNub = matrixX * matrixY * channelNo;
|
||||
} else {
|
||||
upNub = sensoryNerveNub;
|
||||
}
|
||||
} else {
|
||||
upNub = hiddenNerveNub;
|
||||
}
|
||||
if (i == hiddenDepth - 1) {//最后一层隐层神经元z
|
||||
downNub = outNerveNub;
|
||||
} else {
|
||||
downNub = hiddenNerveNub;
|
||||
}
|
||||
HiddenNerve hiddenNerve = new HiddenNerve(j, i + 1, upNub, downNub, studyPoint, initPower, activeFunction, false
|
||||
, rzType, lParam, kernLen, myMatrixX, myMatrixY, false, coreNumber, 0, oneConvRate, false
|
||||
, myCustomEncoding, gaMa, gMaxTh, auto, 1);
|
||||
hiddenNerveList.add(hiddenNerve);
|
||||
}
|
||||
depthNerves.add(hiddenNerveList);
|
||||
}
|
||||
initHiddenNerve();
|
||||
}
|
||||
|
||||
private void initHiddenNerve() {//初始化隐层神经元2
|
||||
for (int i = 0; i < hiddenDepth - 1; i++) {//遍历深度
|
||||
List<Nerve> hiddenNerveList = depthNerves.get(i);//当前遍历隐层神经元
|
||||
List<Nerve> nextHiddenNerveList = depthNerves.get(i + 1);//当前遍历的下一层神经元
|
||||
for (Nerve hiddenNerve : hiddenNerveList) {
|
||||
hiddenNerve.connect(nextHiddenNerveList);
|
||||
}
|
||||
for (Nerve nextHiddenNerve : nextHiddenNerveList) {
|
||||
nextHiddenNerve.connectFather(hiddenNerveList);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user