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基础神经网络的softMax计算性能优化

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This commit is contained in:
李大鹏 2024-04-10 10:57:14 +08:00
parent 1b225aa73b
commit 72a6fb0eb7
3 changed files with 69 additions and 35 deletions
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12
src/main/java/org/wlld/nerveCenter/NerveManager.java
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@ -45,6 +45,7 @@ public class NerveManager {
public void setMatrixMap(Map<Integer, Matrix> matrixMap) {
this.matrixMap = matrixMap;
}
private Map<String, Double> conversion(Map<Integer, Double> map) {
Map<String, Double> cMap = new HashMap<>();
for (Map.Entry<Integer, Double> entry : map.entrySet()) {
@ -60,6 +61,7 @@ public class NerveManager {
}
return cMap;
}
private ModelParameter getDymModelParameter() throws Exception {//获取动态神经元参数
ModelParameter modelParameter = new ModelParameter();
List<DymNerveStudy> dymNerveStudies = new ArrayList<>();//动态神经元隐层
@ -184,7 +186,7 @@ public class NerveManager {
NerveStudy nerveStudy = outStudyNerves.get(i);
outNerve.setThreshold(nerveStudy.getThreshold());
Map<Integer, Double> dendrites = outNerve.getDendrites();
Map<Integer, Double> studyDendrites =unConversion(nerveStudy.getDendrites());
Map<Integer, Double> studyDendrites = unConversion(nerveStudy.getDendrites());
for (Map.Entry<Integer, Double> outEntry : dendrites.entrySet()) {
int key = outEntry.getKey();
dendrites.put(key, studyDendrites.get(key));
@ -246,6 +248,7 @@ public class NerveManager {
List<Nerve> nerveList = depthNerves.get(0);//第一层隐层神经元
//最后一层隐层神经元啊
List<Nerve> lastNerveList = depthNerves.get(depthNerves.size() - 1);
List<OutNerve> myOutNerveList = new ArrayList<>();
//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,
@ -253,16 +256,15 @@ public class NerveManager {
if (isMatrix) {//是卷积层神经网络
outNerve.setMatrixMap(matrixMap);
}
if (isSoftMax) {
SoftMax softMax = new SoftMax(i, outNerveNub, false, outNerve, isShowLog);
softMaxList.add(softMax);
}
//输出层神经元连接最后一层隐层神经元
outNerve.connectFather(lastNerveList);
outNerves.add(outNerve);
myOutNerveList.add(outNerve);
}
//生成softMax层
if (isSoftMax) {//增加softMax层
SoftMax softMax = new SoftMax(outNerveNub, false, myOutNerveList, isShowLog);
softMaxList.add(softMax);
for (Nerve nerve : outNerves) {
nerve.connect(softMaxList);
}

2
src/main/java/org/wlld/nerveEntity/OutNerve.java
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@ -24,7 +24,7 @@ public class OutNerve extends Nerve {
this.isSoftMax = isSoftMax;
}
void getGBySoftMax(double g, long eventId, int id) throws Exception {//接收softMax层回传梯度
void getGBySoftMax(double g, long eventId) throws Exception {//接收softMax层回传梯度
gradient = g;
updatePower(eventId);
}

90
src/main/java/org/wlld/nerveEntity/SoftMax.java
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@ -3,17 +3,18 @@ package org.wlld.nerveEntity;
import org.wlld.config.RZ;
import org.wlld.i.OutBack;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
public class SoftMax extends Nerve {
private OutNerve outNerve;
private boolean isShowLog;
private final List<OutNerve> outNerves;
private final boolean isShowLog;
public SoftMax(int id, int upNub, boolean isDynamic, OutNerve outNerve, boolean isShowLog) throws Exception {
super(id, upNub, "softMax", 0, 0, false, null, isDynamic
public SoftMax(int upNub, boolean isDynamic, List<OutNerve> outNerves, boolean isShowLog) throws Exception {
super(0, upNub, "softMax", 0, 0, false, null, isDynamic
, RZ.NOT_RZ, 0, 0, 0);
this.outNerve = outNerve;
this.outNerves = outNerves;
this.isShowLog = isShowLog;
}
@ -21,24 +22,28 @@ public class SoftMax extends Nerve {
protected void input(long eventId, double parameter, boolean isStudy, Map<Integer, Double> E, OutBack outBack) throws Exception {
boolean allReady = insertParameter(eventId, parameter);
if (allReady) {
double out = softMax(eventId);//输出值
Mes mes = softMax(eventId, isStudy);//输出值
int key = 0;
if (isStudy) {//学习
outNub = out;
if (E.containsKey(getId())) {
this.E = E.get(getId());
} else {
this.E = 0;
for (Map.Entry<Integer, Double> entry : E.entrySet()) {
if (entry.getValue() > 0.9) {
key = entry.getKey();
break;
}
}
if (isShowLog) {
System.out.println("softMax==" + this.E + ",out==" + out + ",nerveId==" + getId());
System.out.println("softMax==" + key + ",out==" + mes.poi + ",nerveId==" + mes.typeID);
}
gradient = -outGradient();//当前梯度变化 把梯度返回
List<Double> errors = error(mes, key);
features.remove(eventId); //清空当前上层输入参数参数
outNerve.getGBySoftMax(gradient, eventId, getId());
int size = outNerves.size();
for (int i = 0; i < size; i++) {
outNerves.get(i).getGBySoftMax(errors.get(i), eventId);
}
} else {//输出
destoryParameter(eventId);
if (outBack != null) {
outBack.getBack(out, getId(), eventId);
outBack.getBack(mes.poi, mes.typeID, eventId);
} else {
throw new Exception("not find outBack");
}
@ -46,26 +51,53 @@ public class SoftMax extends Nerve {
}
}
private double outGradient() {//生成输出层神经元梯度变化
double g = outNub;
if (E == 1) {
//g = ArithUtil.sub(g, 1);
g = g - 1;
private List<Double> error(Mes mes, int key) {
int t = key - 1;
List<Double> softMax = mes.softMax;
List<Double> error = new ArrayList<>();
for (int i = 0; i < softMax.size(); i++) {
double self = softMax.get(i);
double myError;
if (i != t) {
myError = -self;
} else {
myError = 1 - self;
}
error.add(myError);
}
return g;
return error;
}
private double softMax(long eventId) {//计算当前输出结果
private Mes softMax(long eventId, boolean isStudy) {//计算当前输出结果
double sigma = 0;
int id = 0;
double poi = 0;
Mes mes = new Mes();
List<Double> featuresList = features.get(eventId);
double self = featuresList.get(getId() - 1);
double eSelf = Math.exp(self);
for (int i = 0; i < featuresList.size(); i++) {
double value = featuresList.get(i);
// sigma = ArithUtil.add(Math.exp(value), sigma);
for (double value : featuresList) {
sigma = Math.exp(value) + sigma;
}
return eSelf / sigma;//ArithUtil.div(eSelf, sigma);
List<Double> softMax = new ArrayList<>();
for (int i = 0; i < featuresList.size(); i++) {
double eSelf = Math.exp(featuresList.get(i));
double value = eSelf / sigma;
if (isStudy) {
softMax.add(value);
}
if (value > poi) {
poi = value;
id = i + 1;
}
}
mes.softMax = softMax;
mes.typeID = id;
mes.poi = poi;
return mes;
}
static class Mes {
int typeID;
double poi;
List<Double> softMax;
}
}