Add File

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

@@ -0,0 +1,52 @@
+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
+ * 隐层神经元
+ * @date 9:30 上午 2019/12/21
+ */
+public class HiddenNerve extends Nerve {
+    private final int depth;//所处深度
+
+    public HiddenNerve(int id, int depth, int upNub, int downNub, float studyPoint,
+                       boolean init, ActiveFunction activeFunction, int rzType, float lParam, int rnnOutNumber) throws Exception {//隐层神经元
+        super(id, upNub, "HiddenNerve", downNub, studyPoint,
+                init, activeFunction, rzType, lParam, rnnOutNumber);
+        this.depth = depth;
+    }
+
+    @Override
+    public void input(long eventId, float parameter, boolean isKernelStudy, Map<Integer, Float> E
+            , OutBack outBack, boolean isEmbedding, Matrix rnnMatrix) throws Exception {//接收上一层的输入
+        boolean allReady = insertParameter(eventId, parameter, isEmbedding);
+        if (allReady) {//参数齐了，开始计算 sigma - threshold
+            if (isEmbedding && !isKernelStudy) {
+                outBack.getWordVector(getId(), getWOne(eventId));
+                destoryParameter(eventId);
+            } else {
+                float sigma = calculation(eventId, isEmbedding);
+                float out = activeFunction.function(sigma);//激活函数输出数值
+                if (rnnMatrix != null) {//rnn 1改输出值，2查看是否需要转向
+                    out = out + rnnMatrix.getNumber(depth, getId() - 1);
+                }
+                if (isKernelStudy) {
+                    outNub = out;
+                } else {
+                    destoryParameter(eventId);
+                }
+                if (rnnMatrix != null && rnnMatrix.getX() == depth + 1) {//转向输出
+                    sendRnnMessage(eventId, out, isKernelStudy, E, outBack, false, rnnMatrix);
+                } else {
+                    sendMessage(eventId, out, isKernelStudy, E, outBack, false, rnnMatrix);
+                }
+            }
+        }
+    }
+
+}