增加TANH 激活函数，默认激活函数使用TANH

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

@@ -296,7 +296,9 @@ public class Operation {//进行计算
     private void lvq(int tagging, Matrix myMatrix) throws Exception {//LVQ学习
         LVQ lvq = templeConfig.getLvq();
         Matrix vector = MatrixOperation.matrixToVector(myMatrix, true);
-        System.out.println(vector.getString());
+        if (templeConfig.isShowLog()) {
+            System.out.println(vector.getString());
+        }
         MatrixBody matrixBody = new MatrixBody();
         matrixBody.setMatrix(vector);
         matrixBody.setId(tagging);

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

@@ -262,7 +262,7 @@ public class TempleConfig {
 
     private void initNerveManager(boolean initPower, int sensoryNerveNub
             , int deep, double studyPoint) throws Exception {
-        nerveManager = new NerveManager(sensoryNerveNub, 9,
+        nerveManager = new NerveManager(sensoryNerveNub, 6,
                 classificationNub, deep, activeFunction, false, isAccurate, studyPoint);
         nerveManager.init(initPower, false, isShowLog);
     }

src/main/java/org/wlld/imageRecognition/border/LVQ.java

@@ -16,7 +16,7 @@ public class LVQ {
     private int typeNub;//原型聚类个数,即分类个数(需要模型返回)
     private MatrixBody[] model;//原型向量(需要模型返回)
     private List<MatrixBody> matrixList = new ArrayList<>();
-    private double studyPoint = 0.1;//量化学习率
+    private double studyPoint = 0.0001;//量化学习率
     private int length;//向量长度(需要返回)
     private boolean isReady = false;
     private int lvqNub;
@@ -86,8 +86,6 @@ public class LVQ {
             long type = matrixBody.getId();//类别
             double distEnd = 0;
             int id = 0;
-            double dis0 = 0;
-            double dis1 = 1;
             for (int i = 0; i < typeNub; i++) {
                 MatrixBody modelBody = model[i];
                 Matrix modelMatrix = modelBody.getMatrix();
@@ -97,16 +95,10 @@ public class LVQ {
                     id = modelBody.getId();
                     distEnd = dist;
                 }
-                if (i == 0) {
-                    dis0 = dist;
-                } else {
-                    dis1 = dist;
-                }
             }
             MatrixBody modelBody = model[id];
             Matrix modelMatrix = modelBody.getMatrix();
             boolean isRight = id == type;
-            System.out.println("type==" + type + ",dis0==" + dis0 + ",dis1==" + dis1);
             Matrix matrix1 = op(matrix, modelMatrix, isRight);
             modelBody.setMatrix(matrix1);
         }

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

@@ -223,11 +223,13 @@ public abstract class Nerve {
 
     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()));
         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);
             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/test/java/coverTest/FoodTest.java

@@ -21,7 +21,7 @@ public class FoodTest {
 
     public static void food() throws Exception {
         Picture picture = new Picture();
-        TempleConfig templeConfig = new TempleConfig(false, true);
+        TempleConfig templeConfig = new TempleConfig(false, false);
         templeConfig.setClassifier(Classifier.DNN);
         templeConfig.isShowLog(true);
         templeConfig.init(StudyPattern.Accuracy_Pattern, true, 640, 640, 4);
@@ -60,7 +60,7 @@ public class FoodTest {
 //        }
 //        templeConfig.getNormalization().avg();
         for (int j = 0; j < 1; j++) {
-            for (int i = 1; i < 1900; i++) {
+            for (int i = 1; i < 1500; i++) {
                 System.out.println("j==" + j + ",study2==================" + i);
                 //读取本地URL地址图片,并转化成矩阵
                 Matrix a = picture.getImageMatrixByLocal("D:\\share\\picture/a" + i + ".jpg");
@@ -88,7 +88,7 @@ public class FoodTest {
         // Operation operation2 = new Operation(templeConfig2);
         int wrong = 0;
         int allNub = 0;
-        for (int i = 1900; i <= 1998; i++) {
+        for (int i = 1500; i <= 1600; i++) {
             //读取本地URL地址图片,并转化成矩阵
             Matrix a = picture.getImageMatrixByLocal("D:\\share\\picture/a" + i + ".jpg");
             Matrix b = picture.getImageMatrixByLocal("D:\\share\\picture/b" + i + ".jpg");

src/test/java/org/wlld/HelloWorld.java

@@ -79,7 +79,7 @@ public class HelloWorld {
         }
         templeConfig.getNormalization().avg();
         //三阶段学习
-        for (int i = 1; i < 1900; i++) {
+        for (int i = 1; i < 1000; i++) {
             System.out.println("study2==================" + i);
             //读取本地URL地址图片,并转化成矩阵
             Matrix a = picture.getImageMatrixByLocal("D:\\share\\picture/a" + i + ".jpg");