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累计BP

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lidapeng 5 years ago
parent f4b0f91f1d
commit 7c4fb8f2d7
5 changed files with 49 additions and 17 deletions
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47
src/main/java/org/wlld/App.java
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@ -1,11 +1,11 @@
package org.wlld;
import org.wlld.nerveCenter.NerveManager;
import org.wlld.nerveEntity.Nerve;
import org.wlld.nerveEntity.SensoryNerve;
import org.wlld.tools.ArithUtil;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.*;
/**
* 测试入口类!
@ -19,21 +19,42 @@ public class App {
//构建一个神经网络管理器，参数：(感知神经元个数，隐层神经元个数，输出神经元个数，输出神经元深度)
//一个神经网络管理管理一个神经网络学习内容，
NerveManager nerveManager =
new NerveManager(2, 3, 1, 2);
new NerveManager(2, 4, 1, 4);
//开始构建神经网络，参数为是否初始化权重及阈值,若
nerveManager.setStudyPoint(0.1);//设置学习率(取值范围是0-1开区间)，若不设置默认为0.1
nerveManager.init(true);
nerveManager.setStudyPoint(0.2);//设置学习率(取值范围是0-1开区间)，若不设置默认为0.1
nerveManager.setOutBack(new Test());//添加判断回调输出类
List<SensoryNerve> sensoryNerves = nerveManager.getSensoryNerves();
Map<Integer, Double> E = new HashMap<>();
for (int i = 0; i < nerveManager.getOutNerveNub(); i++) {
E.put(i + 1, 1.0);
Map<Integer, Double> E1 = new HashMap<>();
E1.put(1, 1.0);
Map<Integer, Double> E2 = new HashMap<>();
E2.put(1, 0.0);
Random random = new Random();
List<List<Double>> testList = new ArrayList<>();
for (int i = 0; i < 1000; i++) {
List<Double> dm = new ArrayList<>();
dm.add(1.0);
dm.add(0.8);
dm.add(0.03);
dm.add(0.06);
testList.add(dm);
}
for (int i = 0; i < sensoryNerves.size(); i++) {
sensoryNerves.get(i).postMessage(1, 2 + i, true, E);
}
for (int i = 0; i < sensoryNerves.size(); i++) {
sensoryNerves.get(i).postMessage(1, 2 + i, false, E);
for (int i = 0; i < 1000; i++) {
List<Double> ds = testList.get(i);
sensoryNerves.get(0).postMessage(1, ds.get(0), true, E1);
sensoryNerves.get(1).postMessage(1, ds.get(1), true, E1);
sensoryNerves.get(0).postMessage(1, ds.get(2), true, E2);
sensoryNerves.get(1).postMessage(1, ds.get(3), true, E2);
}
Nerve hiddenNerve = nerveManager.getDepthNerves().get(0).get(0);
Nerve outNerver = nerveManager.getOutNevers().get(0);
double hiddenTh = hiddenNerve.getThreshold();//隐层阈值
double outTh = outNerver.getThreshold();//输出阈值
System.out.println("hiddenTh==" + hiddenTh + ",outTh==" + outTh);
sensoryNerves.get(0).postMessage(1, 1.0, false, E1);
sensoryNerves.get(1).postMessage(1, 0.8, false, E1);
sensoryNerves.get(0).postMessage(1, 0.03, false, E2);
sensoryNerves.get(1).postMessage(1, 0.06, false, E2);
}
}

1
src/main/java/org/wlld/nerveEntity/HiddenNerve.java
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@ -27,6 +27,7 @@ public class HiddenNerve extends Nerve {
if (isStudy) {
outNub = out;
} else {
//System.out.println("sigma:" + sigma);
destoryParameter(eventId);
}
// logger.debug("depth:{},myID:{},outPut:{}", depth, getId(), out);

8
src/main/java/org/wlld/nerveEntity/Nerve.java
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@ -93,7 +93,7 @@ public abstract class Nerve {
protected void updatePower(long eventId) throws Exception {//修改阈值
double h = ArithUtil.mul(gradient, studyPoint);//梯度下降
threshold = ArithUtil.sub(threshold, h);//更新阈值
threshold = ArithUtil.add(threshold, -h);//更新阈值
updateW(h, eventId);
sigmaW = 0;//求和结果归零
backSendMessage(eventId);
@ -106,8 +106,8 @@ public abstract class Nerve {
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.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
w = ArithUtil.add(w, wp);//修正后的编号为KEY的上层隐层神经元权重
wg.put(key, dm);//保存上一层权重与梯度的积
dendrites.put(key, w);//保存修正结果
}
@ -140,11 +140,13 @@ public abstract class Nerve {
for (int i = 0; i < featuresList.size(); i++) {
double value = featuresList.get(i);
double w = dendrites.get(i + 1);
//System.out.println("w==" + w + ",value==" + value);
sigma = ArithUtil.add(ArithUtil.mul(w, value), sigma);
//logger.debug("name:{},eventId:{},id:{},myId:{},w:{},value:{}", name, eventId, i + 1, id, w, value);
}
//System.out.println("结束===========" + sigma);
//logger.debug("当前神经元线性变化已经完成,name:{},id:{}", name, getId());
return ArithUtil.sub(sigma, threshold);
return ArithUtil.add(sigma, threshold);
}
private void initPower() {//初始化权重及阈值

9
src/main/java/org/wlld/nerveEntity/OutNerve.java
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@ -13,6 +13,8 @@ import java.util.Map;
public class OutNerve extends Nerve {
// static final Logger logger = LogManager.getLogger(OutNerve.class);
private OutBack outBack;
private long trainNub = 0;//训练次数
private double allE;//训练累计EK
public OutNerve(int id, int upNub, int downNub, double studyPoint, boolean init) {
super(id, upNub, "OutNerve", downNub, studyPoint, init);
@ -31,12 +33,14 @@ public class OutNerve extends Nerve {
double out = activeFunction.sigmoid(sigma);
// logger.debug("myId:{},outPut:{}------END", getId(), out);
if (isStudy) {//输出结果并进行BP调整权重及阈值
trainNub++;//训练次数增加
outNub = out;
this.E = E.get(getId());
gradient = outGradient();//当前梯度变化
//调整权重 修改阈值 并进行反向传播
updatePower(eventId);
} else {//获取最后输出
//System.out.println("当前阈值" + threshold);
destoryParameter(eventId);
if (outBack != null) {
outBack.getBack(out, getId(), eventId);
@ -50,6 +54,9 @@ public class OutNerve extends Nerve {
private double outGradient() {//生成输出层神经元梯度变化
//上层神经元输入值 * 当前神经元梯度*学习率 =该上层输入的神经元权重变化
//当前梯度神经元梯度变化 *学习旅 * -1 = 当前神经元阈值变化
return ArithUtil.mul(activeFunction.sigmoidG(outNub), ArithUtil.sub(E, outNub));
//ArithUtil.sub(E, outNub) 求这个的累计平均值
allE = ArithUtil.add(Math.abs(ArithUtil.sub(E, outNub)), allE);
double avg = ArithUtil.div(allE, trainNub);
return ArithUtil.mul(activeFunction.sigmoidG(outNub), avg);
}
}

1
src/main/java/org/wlld/nerveEntity/SensoryNerve.java
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@ -15,6 +15,7 @@ public class SensoryNerve extends Nerve {
}
public void postMessage(long eventId, double parameter, boolean isStudy, Map<Integer, Double> E) throws Exception {//感知神经元输出
sendMessage(eventId, parameter, isStudy, E);
}

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