增加打印参数

src/main/java/org/wlld/config/RZ.java

@@ -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;
+}

src/main/java/org/wlld/imageRecognition/TempleConfig.java

@@ -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,
+        nerveManager = new NerveManager(sensoryNerveNub, hiddenNerveNub,
-                classificationNub, deep, activeFunction, false, isAccurate, studyPoint);
+                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;

src/main/java/org/wlld/nerveCenter/NerveManager.java

@@ -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);

src/main/java/org/wlld/nerveEntity/HiddenNerve.java

@@ -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
+                       boolean init, ActiveFunction activeFunction, boolean isDynamic,
-        super(id, upNub, "HiddenNerve", downNub, studyPoint, init, activeFunction, isDynamic, isAccurate);
+                       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;
     }
 

src/main/java/org/wlld/nerveEntity/Nerve.java

@@ -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);

src/main/java/org/wlld/nerveEntity/OutNerve.java

@@ -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 {
+            , boolean isShowLog, int rzType, double lParam) throws Exception {
-        super(id, upNub, "OutNerve", downNub, studyPoint, init, activeFunction, isDynamic, isAccurate);
+        super(id, upNub, "OutNerve", downNub, studyPoint, init,
+                activeFunction, isDynamic, isAccurate, rzType, lParam);
         this.isShowLog = isShowLog;
     }
 

src/main/java/org/wlld/nerveEntity/SensoryNerve.java

@@ -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);
     }
 
     /**

src/test/java/org/wlld/NerveDemo1.java

@@ -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