From c20ef7974d0609d3f3b0e81ec8d04951e53b180b Mon Sep 17 00:00:00 2001
From: inter <inter@qq.com>
Date: Thu, 4 Sep 2025 14:09:03 +0800
Subject: [PATCH] Add File

---
 .../dromara/easyai/config/ResnetConfig.java   | 144 ++++++++++++++++++
 1 file changed, 144 insertions(+)
 create mode 100644 src/main/java/org/dromara/easyai/config/ResnetConfig.java

diff --git a/src/main/java/org/dromara/easyai/config/ResnetConfig.java b/src/main/java/org/dromara/easyai/config/ResnetConfig.java
new file mode 100644
index 0000000..14bca25
--- /dev/null
+++ b/src/main/java/org/dromara/easyai/config/ResnetConfig.java
@@ -0,0 +1,144 @@
+package org.dromara.easyai.config;
+
+/**
+ * @author lidapeng
+ * @time 2025/4/11 10:53
+ * @des resNet 配置参数
+ */
+public class ResnetConfig {
+    private int size;//图像尺寸 图像必须为正方形
+    private float studyRate = 0.0025f;//全局学习率
+    private int regularModel = RZ.NOT_RZ;//正则模式
+    private float regular = 0;//正则系数
+    private int hiddenNerveNumber = 16;//线性层隐层神经元数量
+    private int typeNumber = 2;//分类数量
+    private boolean softMax = true;//分类还是拟合
+    private boolean showLog = true;//是否打印参数
+    private int channelNo = 2;//通道数
+    private int hiddenDeep = 1;//线性层隐层神经元深度
+    private int minFeatureSize = 5;//卷积层最小特征大小
+    private float gaMa = 0.9f;//自适应学习率衰减系数
+    private float GMaxTh = 500f;//梯度最大值
+    private boolean auto = true;//是否使用自适应学习率
+    private float GRate = 0.01f;//梯度衰减
+
+    public float getGRate() {
+        return GRate;
+    }
+
+    public void setGRate(float GRate) {
+        this.GRate = GRate;
+    }
+
+    public boolean isAuto() {
+        return auto;
+    }
+
+    public void setAuto(boolean auto) {
+        this.auto = auto;
+    }
+
+    public float getGMaxTh() {
+        return GMaxTh;
+    }
+
+    public void setGMaxTh(float GMaxTh) {
+        this.GMaxTh = GMaxTh;
+    }
+
+    public float getGaMa() {
+        return gaMa;
+    }
+
+    public void setGaMa(float gaMa) {
+        this.gaMa = gaMa;
+    }
+
+    public int getMinFeatureSize() {
+        return minFeatureSize;
+    }
+
+    public void setMinFeatureSize(int minFeatureSize) {
+        this.minFeatureSize = minFeatureSize;
+    }
+
+    public int getHiddenDeep() {
+        return hiddenDeep;
+    }
+
+    public void setHiddenDeep(int hiddenDeep) {
+        this.hiddenDeep = hiddenDeep;
+    }
+
+    public int getChannelNo() {
+        return channelNo;
+    }
+
+    public void setChannelNo(int channelNo) {
+        this.channelNo = channelNo;
+    }
+
+    public boolean isShowLog() {
+        return showLog;
+    }
+
+    public void setShowLog(boolean showLog) {
+        this.showLog = showLog;
+    }
+
+    public int getSize() {
+        return size;
+    }
+
+    public void setSize(int size) {
+        this.size = size;
+    }
+
+    public float getStudyRate() {
+        return studyRate;
+    }
+
+    public void setStudyRate(float studyRate) {
+        this.studyRate = studyRate;
+    }
+
+    public int getRegularModel() {
+        return regularModel;
+    }
+
+    public void setRegularModel(int regularModel) {
+        this.regularModel = regularModel;
+    }
+
+    public float getRegular() {
+        return regular;
+    }
+
+    public void setRegular(float regular) {
+        this.regular = regular;
+    }
+
+    public int getHiddenNerveNumber() {
+        return hiddenNerveNumber;
+    }
+
+    public void setHiddenNerveNumber(int hiddenNerveNumber) {
+        this.hiddenNerveNumber = hiddenNerveNumber;
+    }
+
+    public int getTypeNumber() {
+        return typeNumber;
+    }
+
+    public void setTypeNumber(int typeNumber) {
+        this.typeNumber = typeNumber;
+    }
+
+    public boolean isSoftMax() {
+        return softMax;
+    }
+
+    public void setSoftMax(boolean softMax) {
+        this.softMax = softMax;
+    }
+}