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将期望矩阵的间隔变成1

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pull/10/head
Administrator 5 years ago
parent 1cece25bfe
commit 2907b78ff8
2 changed files with 32 additions and 31 deletions
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1
src/main/java/org/wlld/nerveEntity/OutNerve.java
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@ -72,6 +72,7 @@ public class OutNerve extends Nerve {
if (isKernelStudy) {//回传 if (isKernelStudy) {//回传
Matrix matrix1 = matrixMapE.get(E); Matrix matrix1 = matrixMapE.get(E);
if (isShowLog) { if (isShowLog) {
System.out.println("E======" + E);
System.out.println(myMatrix.getString()); System.out.println(myMatrix.getString());
} }
if (matrix1.getX() <= myMatrix.getX() && matrix1.getY() <= myMatrix.getY()) { if (matrix1.getX() <= myMatrix.getX() && matrix1.getY() <= myMatrix.getY()) {

62
src/test/java/coverTest/FoodTest.java
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@ -21,44 +21,44 @@ public class FoodTest {
public static void food() throws Exception { public static void food() throws Exception {
Picture picture = new Picture(); Picture picture = new Picture();
TempleConfig templeConfig = new TempleConfig(false, false); TempleConfig templeConfig = new TempleConfig(false, true);
templeConfig.setClassifier(Classifier.DNN); templeConfig.setClassifier(Classifier.DNN);
templeConfig.isShowLog(true); templeConfig.isShowLog(true);
templeConfig.init(StudyPattern.Accuracy_Pattern, true, 640, 640, 4); templeConfig.init(StudyPattern.Accuracy_Pattern, true, 640, 640, 4);
ModelParameter modelParameter2 = JSON.parseObject(ModelData.DATA3, ModelParameter.class); // ModelParameter modelParameter2 = JSON.parseObject(ModelData.DATA3, ModelParameter.class);
templeConfig.insertModel(modelParameter2); // templeConfig.insertModel(modelParameter2);
Operation operation = new Operation(templeConfig); Operation operation = new Operation(templeConfig);
// 一阶段 // 一阶段
// for (int j = 0; j < 1; j++) { for (int j = 0; j < 1; j++) {
// for (int i = 1; i < 1900; i++) {//一阶段 for (int i = 1; i < 1500; i++) {//一阶段
// System.out.println("study1===================" + i); System.out.println("study1===================" + i);
// //读取本地URL地址图片,并转化成矩阵 //读取本地URL地址图片,并转化成矩阵
// Matrix a = picture.getImageMatrixByLocal("D:\\share\\picture/a" + i + ".jpg"); Matrix a = picture.getImageMatrixByLocal("D:\\share\\picture/a" + i + ".jpg");
// Matrix b = picture.getImageMatrixByLocal("D:\\share\\picture/b" + i + ".jpg"); Matrix b = picture.getImageMatrixByLocal("D:\\share\\picture/b" + i + ".jpg");
// Matrix c = picture.getImageMatrixByLocal("D:\\share\\picture/c" + i + ".jpg"); Matrix c = picture.getImageMatrixByLocal("D:\\share\\picture/c" + i + ".jpg");
// Matrix d = picture.getImageMatrixByLocal("D:\\share\\picture/d" + i + ".jpg"); Matrix d = picture.getImageMatrixByLocal("D:\\share\\picture/d" + i + ".jpg");
// //将图像矩阵和标注加入进行学习，Accuracy_Pattern 模式 进行第二次学习 //将图像矩阵和标注加入进行学习，Accuracy_Pattern 模式 进行第二次学习
// //第二次学习的时候，第三个参数必须是 true //第二次学习的时候，第三个参数必须是 true
// operation.learning(a, 1, false); operation.learning(a, 1, false);
// operation.learning(b, 2, false); operation.learning(b, 2, false);
// operation.learning(c, 3, false); operation.learning(c, 3, false);
// operation.learning(d, 4, false); operation.learning(d, 4, false);
// } }
// } }
//二阶段 //二阶段
// for (int i = 1; i < 1900; i++) { for (int i = 1; i < 1500; i++) {
// System.out.println("avg==" + i); System.out.println("avg==" + i);
// Matrix a = picture.getImageMatrixByLocal("D:\\share\\picture/a" + i + ".jpg"); Matrix a = picture.getImageMatrixByLocal("D:\\share\\picture/a" + i + ".jpg");
// Matrix b = picture.getImageMatrixByLocal("D:\\share\\picture/b" + i + ".jpg"); Matrix b = picture.getImageMatrixByLocal("D:\\share\\picture/b" + i + ".jpg");
// Matrix c = picture.getImageMatrixByLocal("D:\\share\\picture/c" + i + ".jpg"); Matrix c = picture.getImageMatrixByLocal("D:\\share\\picture/c" + i + ".jpg");
// Matrix d = picture.getImageMatrixByLocal("D:\\share\\picture/d" + i + ".jpg"); Matrix d = picture.getImageMatrixByLocal("D:\\share\\picture/d" + i + ".jpg");
// operation.normalization(a, templeConfig.getConvolutionNerveManager()); operation.normalization(a, templeConfig.getConvolutionNerveManager());
// operation.normalization(b, templeConfig.getConvolutionNerveManager()); operation.normalization(b, templeConfig.getConvolutionNerveManager());
// operation.normalization(c, templeConfig.getConvolutionNerveManager()); operation.normalization(c, templeConfig.getConvolutionNerveManager());
// operation.normalization(d, templeConfig.getConvolutionNerveManager()); operation.normalization(d, templeConfig.getConvolutionNerveManager());
// } }
// templeConfig.getNormalization().avg(); templeConfig.getNormalization().avg();
for (int j = 0; j < 1; j++) { for (int j = 0; j < 1; j++) {
for (int i = 1; i < 1500; i++) { for (int i = 1; i < 1500; i++) {
System.out.println("j==" + j + ",study2==================" + i); System.out.println("j==" + j + ",study2==================" + i);

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