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103
src/main/java/org/dromara/easyai/nerveEntity/SoftMax.java
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103
src/main/java/org/dromara/easyai/nerveEntity/SoftMax.java
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package org.dromara.easyai.nerveEntity;
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import org.dromara.easyai.config.RZ;
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import org.dromara.easyai.i.OutBack;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Map;
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public class SoftMax extends Nerve {
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private final List<OutNerve> outNerves;
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private final boolean isShowLog;
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public SoftMax(int upNub, boolean isDynamic, List<OutNerve> outNerves, boolean isShowLog, int coreNumber) throws Exception {
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super(0, upNub, "softMax", 0, 0, false, null, isDynamic
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, RZ.NOT_RZ, 0, 0, 0, 0, 0, coreNumber, 0
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, 0, false, null, 0.9f, 0.9f, false, 1);
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this.outNerves = outNerves;
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this.isShowLog = isShowLog;
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}
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@Override
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protected void input(long eventId, float parameter, boolean isStudy, Map<Integer, Float> E, OutBack outBack) throws Exception {
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boolean allReady = insertParameter(eventId, parameter);
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if (allReady) {
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Mes mes = softMax(eventId);//输出值
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int key = 0;
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if (isStudy) {//学习
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for (Map.Entry<Integer, Float> entry : E.entrySet()) {
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if (entry.getValue() > 0.9) {
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key = entry.getKey();
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break;
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}
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}
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if (isShowLog) {
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System.out.println("softMax==" + key + ",out==" + mes.poi + ",nerveId==" + mes.typeID);
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}
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List<Float> errors = error(mes, key);
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features.remove(eventId); //清空当前上层输入参数参数
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int size = outNerves.size();
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for (int i = 0; i < size; i++) {
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outNerves.get(i).getGBySoftMax(errors.get(i), eventId);
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}
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} else {//输出
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destoryParameter(eventId);
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if (outBack != null) {
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outBack.getBack(mes.poi, mes.typeID, eventId);
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outBack.getSoftMaxBack(eventId, mes.softMax);
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} else {
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throw new Exception("not find outBack");
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}
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}
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}
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}
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private List<Float> error(Mes mes, int key) {
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int t = key - 1;
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List<Float> softMax = mes.softMax;
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List<Float> error = new ArrayList<>();
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for (int i = 0; i < softMax.size(); i++) {
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float self = softMax.get(i);
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float myError;
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if (i != t) {
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myError = -self;
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} else {
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myError = 1 - self;
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}
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error.add(myError);
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}
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return error;
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}
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private Mes softMax(long eventId) {//计算当前输出结果
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float sigma = 0;
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int id = 0;
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float poi = 0;
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Mes mes = new Mes();
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List<Float> featuresList = features.get(eventId);
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for (float value : featuresList) {
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sigma = (float) Math.exp(value) + sigma;
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}
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List<Float> softMax = new ArrayList<>();
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for (int i = 0; i < featuresList.size(); i++) {
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float eSelf = (float) Math.exp(featuresList.get(i));
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float value = eSelf / sigma;
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softMax.add(value);
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if (value > poi) {
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poi = value;
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id = i + 1;
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}
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}
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mes.softMax = softMax;
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mes.typeID = id;
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mes.poi = poi;
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return mes;
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}
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static class Mes {
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int typeID;
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float poi;
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List<Float> softMax;
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}
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}
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