增加TANH 激活函数，默认激活函数使用TANH

src/main/java/org/wlld/function/Tanh.java

@@ -0,0 +1,18 @@
+package org.wlld.function;
+
+import org.wlld.i.ActiveFunction;
+import org.wlld.tools.ArithUtil;
+
+public class Tanh implements ActiveFunction {
+    @Override
+    public double function(double x) {
+        double son = ArithUtil.sub(Math.exp(x), Math.exp(-x));
+        double mother = ArithUtil.add(Math.exp(x), Math.exp(-x));
+        return ArithUtil.div(son, mother);
+    }
+
+    @Override
+    public double functionG(double out) {
+        return ArithUtil.sub(1, Math.pow(function(out), 2));
+    }
+}

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

@@ -6,6 +6,8 @@ import org.wlld.config.Classifier;
 import org.wlld.config.StudyPattern;
 import org.wlld.function.ReLu;
 import org.wlld.function.Sigmod;
+import org.wlld.function.Tanh;
+import org.wlld.i.ActiveFunction;
 import org.wlld.imageRecognition.border.*;
 import org.wlld.imageRecognition.modelEntity.BoxList;
 import org.wlld.imageRecognition.modelEntity.KBorder;
@@ -49,7 +51,7 @@ public class TempleConfig {
     private double avg = 0;//覆盖均值
     private int sensoryNerveNub;//输入神经元个数
     private boolean isShowLog = false;
-
+    private ActiveFunction activeFunction = new Tanh();
     public boolean isAccurate() {
         return isAccurate;
     }
@@ -58,6 +60,10 @@ public class TempleConfig {
         isAccurate = accurate;
     }
 
+    public void setActiveFunction(ActiveFunction activeFunction) {
+        this.activeFunction = activeFunction;
+    }
+
     public double getAvg() {
         return avg;
     }
@@ -247,7 +253,7 @@ public class TempleConfig {
     private void initNerveManager(boolean initPower, int sensoryNerveNub
             , int deep) throws Exception {
         nerveManager = new NerveManager(sensoryNerveNub, 9,
-                classificationNub, deep, new Sigmod(), false, isAccurate);
+                classificationNub, deep, activeFunction, false, isAccurate);
         nerveManager.init(initPower, false, isShowLog);
     }
 
@@ -461,9 +467,6 @@ public class TempleConfig {
                     vectorK.insertKMatrix(modelParameter.getMatrixK());
                     break;
                 case Classifier.DNN:
-//                    ModelParameter modelParameter1 = new ModelParameter();
-//                    modelParameter1.setDepthNerves(modelParameter.getDepthNerves());
-//                    modelParameter1.setOutNerves(modelParameter.getOutNerves());
                     nerveManager.insertModelParameter(modelParameter);
                     normalization = new Normalization();
                     normalization.setAvg(modelParameter.getDnnAvg());

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

@@ -46,7 +46,7 @@ public class OutNerve extends Nerve {
                 if (E.containsKey(getId())) {
                     this.E = E.get(getId());
                 } else {
-                    this.E = 0;
+                    this.E = -1;
                 }
                 if (isShowLog) {
                     System.out.println("E==" + this.E + ",out==" + out + ",nerveId==" + getId());

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

@@ -127,44 +127,44 @@ public class HelloWorld {
         templeConfig.setClassifier(Classifier.DNN);
         templeConfig.isShowLog(true);
         templeConfig.init(StudyPattern.Accuracy_Pattern, true, 640, 640, 4);
-//        ModelParameter modelParameter2 = JSON.parseObject(ModelData.DATA2, ModelParameter.class);
+        ModelParameter modelParameter2 = JSON.parseObject(ModelData.DATA3, ModelParameter.class);
-//        templeConfig.insertModel(modelParameter2);
+        templeConfig.insertModel(modelParameter2);
         Operation operation = new Operation(templeConfig);
         //a b c d 物品  e是背景
         // 一阶段
-        for (int j = 0; j < 1; j++) {
+//        for (int j = 0; j < 1; j++) {
-            for (int i = 1; i < 1900; i++) {//一阶段
+//            for (int i = 1; i < 1900; i++) {//一阶段
-                System.out.println("study1===================" + i);
+//                System.out.println("study1===================" + i);
-                //读取本地URL地址图片,并转化成矩阵
+//                //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 a = picture.getImageMatrixByLocal("D:\\share\\picture/a" + i + ".jpg");
-                Matrix b = picture.getImageMatrixByLocal("D:\\share\\picture/b" + i + ".jpg");
+//                Matrix b = picture.getImageMatrixByLocal("D:\\share\\picture/b" + i + ".jpg");
-                Matrix c = picture.getImageMatrixByLocal("D:\\share\\picture/c" + i + ".jpg");
+//                Matrix c = picture.getImageMatrixByLocal("D:\\share\\picture/c" + i + ".jpg");
-                Matrix d = picture.getImageMatrixByLocal("D:\\share\\picture/d" + i + ".jpg");
+//                Matrix d = picture.getImageMatrixByLocal("D:\\share\\picture/d" + i + ".jpg");
-                //将图像矩阵和标注加入进行学习，Accuracy_Pattern 模式 进行第二次学习
+//                //将图像矩阵和标注加入进行学习，Accuracy_Pattern 模式 进行第二次学习
-                //第二次学习的时候，第三个参数必须是 true
+//                //第二次学习的时候，第三个参数必须是 true
-                operation.learning(a, 1, false);
+//                operation.learning(a, 1, false);
-                operation.learning(b, 2, false);
+//                operation.learning(b, 2, false);
-                operation.learning(c, 3, false);
+//                operation.learning(c, 3, false);
-                operation.learning(d, 4, false);
+//                operation.learning(d, 4, false);
-            }
+//            }
-        }
+//        }
 
         //二阶段
-        for (int i = 1; i < 1900; i++) {
+//        for (int i = 1; i < 1900; i++) {
-            System.out.println("avg==" + i);
+//            System.out.println("avg==" + i);
-            Matrix a = picture.getImageMatrixByLocal("D:\\share\\picture/a" + i + ".jpg");
+//            Matrix a = picture.getImageMatrixByLocal("D:\\share\\picture/a" + i + ".jpg");
-            Matrix b = picture.getImageMatrixByLocal("D:\\share\\picture/b" + i + ".jpg");
+//            Matrix b = picture.getImageMatrixByLocal("D:\\share\\picture/b" + i + ".jpg");
-            Matrix c = picture.getImageMatrixByLocal("D:\\share\\picture/c" + i + ".jpg");
+//            Matrix c = picture.getImageMatrixByLocal("D:\\share\\picture/c" + i + ".jpg");
-            Matrix d = picture.getImageMatrixByLocal("D:\\share\\picture/d" + i + ".jpg");
+//            Matrix d = picture.getImageMatrixByLocal("D:\\share\\picture/d" + i + ".jpg");
-            operation.normalization(a, templeConfig.getConvolutionNerveManager());
+//            operation.normalization(a, templeConfig.getConvolutionNerveManager());
-            operation.normalization(b, templeConfig.getConvolutionNerveManager());
+//            operation.normalization(b, templeConfig.getConvolutionNerveManager());
-            operation.normalization(c, templeConfig.getConvolutionNerveManager());
+//            operation.normalization(c, templeConfig.getConvolutionNerveManager());
-            operation.normalization(d, templeConfig.getConvolutionNerveManager());
+//            operation.normalization(d, templeConfig.getConvolutionNerveManager());
-        }
+//        }
-        templeConfig.getNormalization().avg();
+//        templeConfig.getNormalization().avg();
         for (int j = 0; j < 1; j++) {
             for (int i = 1; i < 1900; i++) {
-                System.out.println("study2==================" + 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");
                 Matrix b = picture.getImageMatrixByLocal("D:\\share\\picture/b" + i + ".jpg");