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

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+package org.dromara.easyai.rnnNerveEntity;
+
+import org.dromara.easyai.matrixTools.Matrix;
+import org.dromara.easyai.i.ActiveFunction;
+import org.dromara.easyai.i.OutBack;
+
+import java.util.Map;
+
+/**
+ * @author lidapeng
+ * 输出神经元
+ * @date 11:25 上午 2019/12/21
+ */
+public class OutNerve extends Nerve {
+    private final boolean isShowLog;
+    private final boolean isSoftMax;
+
+    public OutNerve(int id, int upNub, int downNub, float studyPoint, boolean init,
+                    ActiveFunction activeFunction, boolean isShowLog, int rzType, float lParam, boolean isSoftMax) throws Exception {
+        super(id, upNub, "OutNerve", downNub, studyPoint, init,
+                activeFunction, rzType, lParam, 0);
+        this.isShowLog = isShowLog;
+        this.isSoftMax = isSoftMax;
+    }
+
+    void getGBySoftMax(float g, long eventId) throws Exception {//接收softMax层回传梯度
+        gradient = g;
+        updatePower(eventId);
+    }
+
+
+    @Override
+    public void input(long eventId, float parameter, boolean isStudy, Map<Integer, Float> E
+            , OutBack outBack, boolean isEmbedding, Matrix rnnMatrix) throws Exception {
+        boolean allReady = insertParameter(eventId, parameter, false);
+        if (allReady) {//参数齐了，开始计算 sigma - threshold
+            float sigma = calculation(eventId, false);
+            if (isSoftMax) {
+                if (!isStudy) {
+                    destoryParameter(eventId);
+                }
+                sendMessage(eventId, sigma, isStudy, E, outBack, false, rnnMatrix);
+            } else {
+                float out = activeFunction.function(sigma);
+                if (isStudy) {//输出结果并进行BP调整权重及阈值
+                    outNub = out;
+                    if (E.containsKey(getId())) {
+                        this.E = E.get(getId());
+                    } else {
+                        this.E = 0;
+                    }
+                    if (isShowLog) {
+                        System.out.println("E==" + this.E + ",out==" + out + ",nerveId==" + 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
+                    destoryParameter(eventId);
+                    if (outBack != null) {
+                        outBack.getBack(out, getId(), eventId);
+                    } else {
+                        throw new Exception("not find outBack");
+                    }
+                }
+            }
+        }
+    }
+
+
+    private float 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 activeFunction.functionG(outNub) * (E - outNub);
+    }
+}