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src/main/java/org/dromara/easyai/rnnNerveEntity/Nerve.java

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+package org.dromara.easyai.rnnNerveEntity;
+
+
+import org.dromara.easyai.matrixTools.Matrix;
+import org.dromara.easyai.matrixTools.MatrixOperation;
+import org.dromara.easyai.config.RZ;
+import org.dromara.easyai.i.ActiveFunction;
+import org.dromara.easyai.i.OutBack;
+
+import java.util.*;
+
+/**
+ * @author lidapeng
+ * 神经元，所有类别神经元都要继承的类，具有公用属性
+ * @date 9:36 上午 2019/12/21
+ */
+public abstract class Nerve {
+    private final List<Nerve> son = new ArrayList<>();//轴突下一层的连接神经元
+    private final List<Nerve> rnnOut = new ArrayList<>();//rnn隐层输出神经元集合
+    private final List<Nerve> father = new ArrayList<>();//树突上一层的连接神经元
+    protected Map<Integer, Float> dendrites = new HashMap<>();//上一层权重(需要取出)
+    protected Map<Integer, Float> wg = new HashMap<>();//上一层权重与梯度的积
+    private final int id;//同级神经元编号,注意在同层编号中ID应有唯一性
+    boolean fromOutNerve = false;//是否是输出神经元
+    protected int upNub;//上一层神经元数量
+    protected int downNub;//下一层神经元的数量
+    protected int rnnOutNub;//rnn输出神经元数量
+    protected Map<Long, List<Float>> features = new HashMap<>();//上一层神经元输入的数值
+    protected Matrix nerveMatrix;//权重矩阵可获取及注入
+    protected float threshold;//此神经元的阈值需要取出
+    protected String name;//该神经元所属类型
+    protected float outNub;//输出数值（ps:只有训练模式的时候才可保存输出过的数值）
+    protected float E;//模板期望值
+    protected float gradient;//当前梯度
+    protected float studyPoint;
+    protected float sigmaW;//对上一层权重与上一层梯度的积进行求和
+    private int backNub = 0;//当前节点被反向传播的次数
+    protected ActiveFunction activeFunction;
+    private final int rzType;//正则化类型，默认不进行正则化
+    private final float lParam;//正则参数
+    private final Map<Long, Integer> embeddingIndex = new HashMap<>();//记录词向量下标位置
+
+    public Map<Integer, Float> getDendrites() {
+        return dendrites;
+    }
+
+    public Matrix getNerveMatrix() {
+        return nerveMatrix;
+    }
+
+    public void setNerveMatrix(Matrix nerveMatrix) {
+        this.nerveMatrix = nerveMatrix;
+    }
+
+    public void setDendrites(Map<Integer, Float> dendrites) {
+        this.dendrites = dendrites;
+    }
+
+    public float getThreshold() {
+        return threshold;
+    }
+
+    public void setThreshold(float threshold) {
+        this.threshold = threshold;
+    }
+
+    protected Nerve(int id, int upNub, String name, int downNub,
+                    float studyPoint, boolean init, ActiveFunction activeFunction, int rzType, float lParam, int rnnOutNub) throws Exception {//该神经元在同层神经元中的编号
+        this.id = id;
+        this.upNub = upNub;
+        this.name = name;
+        this.downNub = downNub;
+        this.studyPoint = studyPoint;
+        this.activeFunction = activeFunction;
+        this.rzType = rzType;
+        this.lParam = lParam;
+        this.rnnOutNub = rnnOutNub;
+        if (name.equals("OutNerve")) {
+            fromOutNerve = true;
+        }
+        initPower(init);//生成随机权重
+    }
+
+    protected void setStudyPoint(float studyPoint) {
+        this.studyPoint = studyPoint;
+    }
+
+    public void sendMessage(long eventId, float parameter, boolean isStudy, Map<Integer, Float> E
+            , OutBack outBack, boolean isEmbedding, Matrix rnnMatrix) throws Exception {
+        if (!son.isEmpty()) {
+            for (Nerve nerve : son) {
+                nerve.input(eventId, parameter, isStudy, E, outBack, isEmbedding, rnnMatrix);
+            }
+        } else {
+            throw new Exception("this layer is lastIndex");
+        }
+    }
+
+    public void sendRnnMessage(long eventId, float parameter, boolean isStudy, Map<Integer, Float> E
+            , OutBack outBack, boolean isEmbedding, Matrix rnnMatrix) throws Exception {
+        if (!rnnOut.isEmpty()) {
+            for (Nerve nerve : rnnOut) {
+                nerve.input(eventId, parameter, isStudy, E, outBack, isEmbedding, rnnMatrix);
+            }
+        } else {
+            throw new Exception("this layer is lastIndex");
+        }
+    }
+
+
+    private void backSendMessage(long eventId, boolean fromOutNerve) throws Exception {//反向传播
+        if (!father.isEmpty()) {
+            for (int i = 0; i < father.size(); i++) {
+                father.get(i).backGetMessage(wg.get(i + 1), eventId, fromOutNerve);
+            }
+        }
+    }
+
+
+    protected void input(long eventId, float parameter, boolean isStudy
+            , Map<Integer, Float> E, OutBack imageBack, boolean isEmbedding, Matrix rnnMatrix) throws Exception {//输入参数
+
+    }
+
+
+    private void backGetMessage(float parameter, long eventId, boolean fromOutNerve) throws Exception {//反向传播
+        backNub++;
+        //sigmaW = ArithUtil.add(sigmaW, parameter);
+        sigmaW = sigmaW + parameter;
+        int number;
+        if (fromOutNerve) {
+            number = rnnOutNub;
+        } else {
+            number = downNub;
+        }
+        if (backNub == number) {//进行新的梯度计算
+            backNub = 0;
+            gradient = activeFunction.functionG(outNub) * sigmaW;
+            updatePower(eventId);//修改阈值
+        }
+    }
+
+    protected void updatePower(long eventId) throws Exception {//修改阈值
+        //float h = ArithUtil.mul(gradient, studyPoint);//梯度下降
+        float h = gradient * studyPoint;
+        //threshold = ArithUtil.add(threshold, -h);//更新阈值
+        threshold = threshold - h;
+        updateW(h, eventId);
+        sigmaW = 0;//求和结果归零
+        backSendMessage(eventId, fromOutNerve);
+    }
+
+    private float regularization(float w, float param) {//正则化类型
+        float re = 0.0F;
+        if (rzType != RZ.NOT_RZ) {
+            if (rzType == RZ.L2) {
+                //re = ArithUtil.mul(param, -w);
+                re = param * -w;
+            } else if (rzType == RZ.L1) {
+                if (w > 0) {
+                    re = -param;
+                } else if (w < 0) {
+                    re = param;
+                }
+            }
+        }
+        return re;
+    }
+
+    private void updateW(float h, long eventId) {//h是学习率 * 当前g（梯度）
+        List<Float> list = features.get(eventId);
+        float param = 0;
+        if (rzType != RZ.NOT_RZ) {
+            double sigma = 0;
+            for (Map.Entry<Integer, Float> entry : dendrites.entrySet()) {
+                if (rzType == RZ.L2) {
+                    sigma = sigma + (float) Math.pow(entry.getValue(), 2);
+                } else {
+                    sigma = sigma + (float) Math.abs(entry.getValue());
+                }
+            }
+            param = (float) sigma * lParam * studyPoint;
+        }
+        for (Map.Entry<Integer, Float> entry : dendrites.entrySet()) {
+            int key = entry.getKey();//上层隐层神经元的编号
+            float w = entry.getValue();//接收到编号为KEY的上层隐层神经元的权重
+            float bn = list.get(key - 1);//接收到编号为KEY的上层隐层神经元的输入
+            float wp = bn * h;
+            float dm = w * gradient;
+            float regular = regularization(w, param);//正则化抑制权重s
+            w = w + regular;
+            w = w + wp;
+            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
+        }
+        features.remove(eventId); //清空当前上层输入参数参数
+    }
+
+    protected boolean insertParameter(long eventId, float parameter, boolean embedding) {//添加参数
+        boolean allReady = false;
+        List<Float> featuresList;
+        if (features.containsKey(eventId)) {
+            featuresList = features.get(eventId);
+        } else {
+            featuresList = new ArrayList<>();
+            features.put(eventId, featuresList);
+        }
+        if (embedding && parameter > 0.5) {
+            embeddingIndex.put(eventId, featuresList.size());
+        }
+        featuresList.add(parameter);
+        if (featuresList.size() >= upNub) {
+            allReady = true;
+        }
+        return allReady;
+    }
+
+    protected void destoryParameter(long eventId) {//销毁参数
+        features.remove(eventId);
+    }
+
+    protected float getWOne(long eventId) {
+        int index = embeddingIndex.get(eventId);
+        return dendrites.get(index + 1);
+    }
+
+    protected float calculation(long eventId, boolean isEmbedding) {//计算当前输出结果
+        float sigma = 0;
+        List<Float> featuresList = features.get(eventId);
+        if (!isEmbedding) {
+            for (int i = 0; i < featuresList.size(); i++) {
+                float value = featuresList.get(i);
+                float w = dendrites.get(i + 1);//当value不为0的时候把w取出来
+                sigma = w * value + sigma;
+            }
+        } else {
+            int index = embeddingIndex.get(eventId);
+            sigma = featuresList.get(index) * dendrites.get(index + 1);
+            embeddingIndex.remove(eventId);
+        }
+        return sigma - threshold;//ArithUtil.sub(sigma, threshold);
+    }
+
+    private void initPower(boolean init) {//初始化权重及阈值
+        Random random = new Random();
+        if (upNub > 0) {//输入个数
+            float sh = (float) Math.sqrt(upNub);
+            for (int i = 1; i < upNub + 1; i++) {
+                float nub = 0;
+                if (init) {
+                    nub = random.nextFloat() / sh;
+                }
+                dendrites.put(i, nub);//random.nextFloat()
+            }
+            //生成随机阈值
+            float nub = 0;
+            if (init) {
+                nub = random.nextFloat() / sh;
+            }
+            threshold = nub;
+        }
+    }
+
+    public int getId() {
+        return id;
+    }
+
+
+    public void connect(List<Nerve> nerveList) {
+        son.addAll(nerveList);//连接下一层
+    }
+
+    public void connectOut(List<Nerve> nerveList) {
+        rnnOut.addAll(nerveList);
+    }
+
+    public void connectFather(List<Nerve> nerveList) {
+        father.addAll(nerveList);//连接上一层
+    }
+}