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src/main/java/org/dromara/easyai/nerveEntity/ConvParameter.java
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165
src/main/java/org/dromara/easyai/nerveEntity/ConvParameter.java
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package org.dromara.easyai.nerveEntity;
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import org.dromara.easyai.matrixTools.Matrix;
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import java.util.ArrayList;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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/**
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* @author lidapeng
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* @time 2025/2/27 20:21
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* @des 卷积参数保存
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*/
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public class ConvParameter {
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private final List<Matrix> nerveMatrixList = new ArrayList<>();//下采样卷积权重矩阵 需取出模型
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private final List<Matrix> dymStudyRateList = new ArrayList<>();//动态学习率
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private final List<ConvSize> convSizeList = new ArrayList<>();
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private final List<Matrix> im2colMatrixList = new ArrayList<>();
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private final List<Matrix> outMatrixList = new ArrayList<>();
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private List<List<Float>> oneConvPower;//1*1卷积核 需取出模型
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private List<List<Float>> oneDymStudyRateList;//
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private List<Float> upOneDymStudyRateList;
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private List<Float> upOneConvPower;//上采样降维卷积核 需要取出模型
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private List<Matrix> featureMatrixList;//所有通道特征矩阵集合
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private final List<Matrix> upDymStudyRateList = new ArrayList<>();
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private final List<Matrix> upNerveMatrixList = new ArrayList<>();//上采样卷积权重 需要取出模型
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private List<Matrix> upFeatureMatrixList;//上采样输入特征
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private List<Matrix> upOutMatrixList;//上卷积输出矩阵集合
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private final Map<Long, List<Matrix>> featureMap = new HashMap<>();
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private int outX;
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private int outY;
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private int encoderX;
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private int encoderY;
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private float studyRateTh = 0;//记录线性模型动态偏移值学习率
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public float getStudyRateTh() {
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return studyRateTh;
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}
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public void setStudyRateTh(float studyRateTh) {
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this.studyRateTh = studyRateTh;
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}
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public List<Matrix> getUpDymStudyRateList() {
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return upDymStudyRateList;
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}
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public List<Float> getUpOneDymStudyRateList() {
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return upOneDymStudyRateList;
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}
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public void setUpOneDymStudyRateList(List<Float> upOneDymStudyRateList) {
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this.upOneDymStudyRateList = upOneDymStudyRateList;
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}
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public List<List<Float>> getOneDymStudyRateList() {
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return oneDymStudyRateList;
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}
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public void setOneDymStudyRateList(List<List<Float>> oneDymStudyRateList) {
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this.oneDymStudyRateList = oneDymStudyRateList;
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}
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public List<Matrix> getDymStudyRateList() {
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return dymStudyRateList;
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}
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public List<Matrix> getUpFeatureMatrixList() {
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return upFeatureMatrixList;
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}
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public void setUpFeatureMatrixList(List<Matrix> upFeatureMatrixList) {
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this.upFeatureMatrixList = upFeatureMatrixList;
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}
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public List<Matrix> getUpOutMatrixList() {
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return upOutMatrixList;
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}
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public void setUpOutMatrixList(List<Matrix> upOutMatrixList) {
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this.upOutMatrixList = upOutMatrixList;
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}
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public List<Float> getUpOneConvPower() {
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return upOneConvPower;
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}
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public void setUpOneConvPower(List<Float> upOneConvPower) {
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this.upOneConvPower = upOneConvPower;
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}
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public int getEncoderX() {
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return encoderX;
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}
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public void setEncoderX(int encoderX) {
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this.encoderX = encoderX;
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}
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public int getEncoderY() {
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return encoderY;
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}
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public void setEncoderY(int encoderY) {
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this.encoderY = encoderY;
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}
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public Map<Long, List<Matrix>> getFeatureMap() {
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return featureMap;
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}
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public List<Matrix> getUpNerveMatrixList() {
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return upNerveMatrixList;
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}
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public int getOutX() {
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return outX;
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}
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public void setOutX(int outX) {
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this.outX = outX;
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}
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public int getOutY() {
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return outY;
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}
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public void setOutY(int outY) {
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this.outY = outY;
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}
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public List<Matrix> getNerveMatrixList() {
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return nerveMatrixList;
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}
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public List<ConvSize> getConvSizeList() {
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return convSizeList;
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}
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public List<Matrix> getIm2colMatrixList() {
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return im2colMatrixList;
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}
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public List<Matrix> getOutMatrixList() {
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return outMatrixList;
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}
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public List<List<Float>> getOneConvPower() {
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return oneConvPower;
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}
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public void setOneConvPower(List<List<Float>> oneConvPower) {
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this.oneConvPower = oneConvPower;
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}
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public List<Matrix> getFeatureMatrixList() {
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return featureMatrixList;
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}
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public void setFeatureMatrixList(List<Matrix> featureMatrixList) {
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this.featureMatrixList = featureMatrixList;
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}
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}
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