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增加打印参数

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pull/10/head
lidapeng 5 years ago
parent 2907b78ff8
commit eb92d8c8ef
8 changed files with 78 additions and 18 deletions
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12
src/main/java/org/wlld/config/RZ.java
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@ -0,0 +1,12 @@
package org.wlld.config;
/**
* @author lidapeng
* @description 正则化选项
* @date 8:55 下午 2020/3/21
*/
public class RZ {
public static final int NOT_RZ = 0;
public static final int L1 = 1;
public static final int L2 = 2;
}

22
src/main/java/org/wlld/imageRecognition/TempleConfig.java
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@ -3,6 +3,7 @@ package org.wlld.imageRecognition;
import org.wlld.MatrixTools.Matrix;
import org.wlld.MatrixTools.MatrixOperation;
import org.wlld.config.Classifier;
import org.wlld.config.RZ;
import org.wlld.config.StudyPattern;
import org.wlld.function.ReLu;
import org.wlld.function.Sigmod;
@ -54,6 +55,17 @@ public class TempleConfig {
private ActiveFunction activeFunction = new Tanh();
private double studyPoint = 0;
private double matrixWidth = 1;//期望矩阵间隔
private int rzType = RZ.NOT_RZ;//正则化类型，默认不进行正则化
private double lParam = 0;//正则参数
private int hiddenNerveNub = 9;//隐层神经元个数
public void setRzType(int rzType) {
this.rzType = rzType;
}
public void setlParam(double lParam) {
this.lParam = lParam;
}
public void setMatrixWidth(double matrixWidth) {
this.matrixWidth = matrixWidth;
@ -120,7 +132,7 @@ public class TempleConfig {
return convolutionNerveManagerB;
}
public void finishStudy() throws Exception {//结束
public void finishStudy() throws Exception {//结束学习
switch (classifier) {
case Classifier.LVQ:
lvq.start();
@ -267,8 +279,9 @@ public class TempleConfig {
private void initNerveManager(boolean initPower, int sensoryNerveNub
, int deep, double studyPoint) throws Exception {
nerveManager = new NerveManager(sensoryNerveNub, 6,
classificationNub, deep, activeFunction, false, isAccurate, studyPoint);
nerveManager = new NerveManager(sensoryNerveNub, hiddenNerveNub,
classificationNub, deep, activeFunction,
false, isAccurate, studyPoint, rzType, lParam);
nerveManager.init(initPower, false, isShowLog);
}
@ -320,7 +333,8 @@ public class TempleConfig {
private NerveManager initNerveManager(Map<Integer, Matrix> matrixMap, boolean initPower, int deep) throws Exception {
//初始化卷积神经网络
NerveManager convolutionNerveManager = new NerveManager(1, 1,
1, deep - 1, new ReLu(), true, isAccurate, studyPoint);
1, deep - 1, new ReLu(),
true, isAccurate, studyPoint, rzType, lParam);
convolutionNerveManager.setMatrixMap(matrixMap);//给卷积网络管理器注入期望矩阵
convolutionNerveManager.init(initPower, true, isShowLog);
return convolutionNerveManager;

12
src/main/java/org/wlld/nerveCenter/NerveManager.java
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@ -31,6 +31,8 @@ public class NerveManager {
private boolean isDynamic;//是否是动态神经网络
private List<Double> studyList = new ArrayList<>();
private boolean isAccurate;//是否保留精度
private int rzType;//正则化类型，默认不进行正则化
private double lParam;//正则参数
public List<Double> getStudyList() {//查看每一次的学习率
return studyList;
@ -194,11 +196,13 @@ public class NerveManager {
* @param activeFunction 激活函数
* @param isDynamic 是否是动态神经元
* @param isAccurate 是否保留精度
* @param rzType 正则函数
* @param lParam 正则系数
* @throws Exception 如果参数错误则抛异常
*/
public NerveManager(int sensoryNerveNub, int hiddenNerveNub, int outNerveNub
, int hiddenDepth, ActiveFunction activeFunction, boolean isDynamic, boolean isAccurate,
double studyPoint) throws Exception {
double studyPoint, int rzType, double lParam) throws Exception {
if (sensoryNerveNub > 0 && hiddenNerveNub > 0 && outNerveNub > 0 && hiddenDepth > 0 && activeFunction != null) {
this.hiddenNerveNub = hiddenNerveNub;
this.sensoryNerveNub = sensoryNerveNub;
@ -207,6 +211,8 @@ public class NerveManager {
this.activeFunction = activeFunction;
this.isDynamic = isDynamic;
this.isAccurate = isAccurate;
this.rzType = rzType;
this.lParam = lParam;
if (studyPoint > 0 && studyPoint < 1) {
this.studyPoint = studyPoint;
}
@ -259,7 +265,7 @@ public class NerveManager {
//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, isMatrix, isAccurate, isShowLog);
activeFunction, isMatrix, isAccurate, isShowLog, rzType, lParam);
if (isMatrix) {//是卷积层神经网络
outNerve.setMatrixMap(matrixMap);
}
@ -306,7 +312,7 @@ public class NerveManager {
downNub = hiddenNerveNub;
}
HiddenNerve hiddenNerve = new HiddenNerve(j, i + 1, upNub, downNub, studyPoint, initPower, activeFunction, isMatrix
, isAccurate);
, isAccurate, rzType, lParam);
hiddenNerveList.add(hiddenNerve);
}
depthNerves.add(hiddenNerveList);

6
src/main/java/org/wlld/nerveEntity/HiddenNerve.java
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@ -17,8 +17,10 @@ public class HiddenNerve extends Nerve {
private int depth;//所处深度
public HiddenNerve(int id, int depth, int upNub, int downNub, double studyPoint,
boolean init, ActiveFunction activeFunction, boolean isDynamic, boolean isAccurate) throws Exception {//隐层神经元
super(id, upNub, "HiddenNerve", downNub, studyPoint, init, activeFunction, isDynamic, isAccurate);
boolean init, ActiveFunction activeFunction, boolean isDynamic,
boolean isAccurate, int rzType, double lParam) throws Exception {//隐层神经元
super(id, upNub, "HiddenNerve", downNub, studyPoint,
init, activeFunction, isDynamic, isAccurate, rzType, lParam);
this.depth = depth;
}

29
src/main/java/org/wlld/nerveEntity/Nerve.java
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@ -3,6 +3,7 @@ package org.wlld.nerveEntity;
import org.wlld.MatrixTools.Matrix;
import org.wlld.MatrixTools.MatrixOperation;
import org.wlld.config.RZ;
import org.wlld.i.ActiveFunction;
import org.wlld.i.OutBack;
import org.wlld.tools.ArithUtil;
@ -35,6 +36,8 @@ public abstract class Nerve {
private int backNub = 0;//当前节点被反向传播的次数
protected ActiveFunction activeFunction;
private boolean isAccurate = false;//是否保留精度
private int rzType;//正则化类型，默认不进行正则化
private double lParam;//正则参数
public Map<Integer, Double> getDendrites() {
return dendrites;
@ -66,7 +69,7 @@ public abstract class Nerve {
protected Nerve(int id, int upNub, String name, int downNub,
double studyPoint, boolean init, ActiveFunction activeFunction
, boolean isDynamic, boolean isAccurate) throws Exception {//该神经元在同层神经元中的编号
, boolean isDynamic, boolean isAccurate, int rzType, double lParam) throws Exception {//该神经元在同层神经元中的编号
this.id = id;
this.upNub = upNub;
this.name = name;
@ -74,8 +77,9 @@ public abstract class Nerve {
this.studyPoint = studyPoint;
this.activeFunction = activeFunction;
this.isAccurate = isAccurate;
this.rzType = rzType;
this.lParam = lParam;
initPower(init, isDynamic);//生成随机权重
}
protected void setStudyPoint(double studyPoint) {
@ -221,15 +225,32 @@ public abstract class Nerve {
backSendMessage(eventId);
}
private double regularization(double w, double param) {//正则化类型
double re = 0.0;
if (rzType != RZ.NOT_RZ) {
if (rzType == RZ.L2) {
re = ArithUtil.mul(param, -w);
} else if (rzType == RZ.L1) {
if (w > 0) {
re = -param;
} else if (w < 0) {
re = param;
}
}
}
return re;
}
private void updateW(double h, long eventId) {//h是学习率 * 当前g（梯度）
List<Double> list = features.get(eventId);
double stop = ArithUtil.sub(1, ArithUtil.div(ArithUtil.mul(studyPoint, 0.015), dendrites.size()));
double param = ArithUtil.div(ArithUtil.mul(studyPoint, lParam), dendrites.size());
for (Map.Entry<Integer, Double> entry : dendrites.entrySet()) {
int key = entry.getKey();//上层隐层神经元的编号
double w = entry.getValue();//接收到编号为KEY的上层隐层神经元的权重
double bn = list.get(key - 1);//接收到编号为KEY的上层隐层神经元的输入
double wp = ArithUtil.mul(bn, h);//编号为KEY的上层隐层神经元权重的变化值
w = ArithUtil.mul(w, stop);
double regular = regularization(w, param);//正则化抑制权重s
w = ArithUtil.add(w, regular);
w = ArithUtil.add(w, wp);//修正后的编号为KEY的上层隐层神经元权重
double dm = ArithUtil.mul(w, gradient);//返回给相对应的神经元
// System.out.println("allG==" + allG + ",dm==" + dm);

5
src/main/java/org/wlld/nerveEntity/OutNerve.java
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@ -23,8 +23,9 @@ public class OutNerve extends Nerve {
public OutNerve(int id, int upNub, int downNub, double studyPoint, boolean init,
ActiveFunction activeFunction, boolean isDynamic, boolean isAccurate
, boolean isShowLog) throws Exception {
super(id, upNub, "OutNerve", downNub, studyPoint, init, activeFunction, isDynamic, isAccurate);
, boolean isShowLog, int rzType, double lParam) throws Exception {
super(id, upNub, "OutNerve", downNub, studyPoint, init,
activeFunction, isDynamic, isAccurate, rzType, lParam);
this.isShowLog = isShowLog;
}

3
src/main/java/org/wlld/nerveEntity/SensoryNerve.java
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@ -15,7 +15,8 @@ import java.util.Map;
public class SensoryNerve extends Nerve {
public SensoryNerve(int id, int upNub) throws Exception {
super(id, upNub, "SensoryNerve", 0, 0.1, false, null, false, false);
super(id, upNub, "SensoryNerve", 0, 0.1, false,
null, false, false, 0, 0);
}
/**

7
src/test/java/org/wlld/NerveDemo1.java
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@ -1,6 +1,7 @@
package org.wlld;
import org.wlld.MatrixTools.Matrix;
import org.wlld.config.RZ;
import org.wlld.function.Sigmod;
import org.wlld.i.OutBack;
import org.wlld.nerveCenter.NerveManager;
@ -32,7 +33,8 @@ public class NerveDemo1 {
* @param activeFunction 激活函数
* @param isDynamic 是否是动态神经元
*/
NerveManager nerveManager = new NerveManager(2, 6, 1, 4, new Sigmod(), false, true, 0);
NerveManager nerveManager = new NerveManager(2, 6, 1, 4, new Sigmod(),
false, true, 0, RZ.NOT_RZ, 0);
nerveManager.init(true, false, false);
@ -108,7 +110,8 @@ public class NerveDemo1 {
public static void test3() throws Exception {
NerveManager nerveManager = new NerveManager(3, 6, 3
, 3, new Sigmod(), false, true, 0);
, 3, new Sigmod(),
false, true, 0, RZ.NOT_RZ, 0);
nerveManager.init(true, false, false);//初始化
List<Map<Integer, Double>> data = new ArrayList<>();//正样本
List<Map<Integer, Double>> dataB = new ArrayList<>();//负样本

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