leecode更新

Leecode/src/main/java/com/markilue/leecode/backtrace/FindSubsequences.java

@@ -0,0 +1,213 @@
+package com.markilue.leecode.backtrace;
+
+import org.junit.Test;
+
+import java.util.ArrayList;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Set;
+
+/**
+ * @BelongsProject: Leecode
+ * @BelongsPackage: com.markilue.leecode.backtrace
+ * @Author: markilue
+ * @CreateTime: 2022-10-19  10:06
+ * @Description: TODO  力扣491题  递增子序列：
+ * 给你一个整数数组 nums ，找出并返回所有该数组中不同的递增子序列，递增子序列中 至少有两个元素 。你可以按 任意顺序 返回答案。
+ * 数组中可能含有重复元素，如出现两个整数相等，也可以视作递增序列的一种特殊情况。
+ * @Version: 1.0
+ */
+public class FindSubsequences {
+
+
+    @Test
+    public void test() {
+        int[] nums = {4,6,7,7};
+        System.out.println(findSubsequences(nums));
+    }
+
+    @Test
+    public void test1() {
+        int[] nums = {4,4,3,2,1};
+        System.out.println(findSubsequences(nums));
+    }
+
+    @Test
+    public void test2() {
+        int[] nums = {1,2,3,4,5,6,7,8,9,10,1,1,1,1,1};
+        System.out.println(findSubsequences(nums));
+        System.out.println(result.size());
+    }
+
+    List<List<Integer>> result = new ArrayList<>();
+    List<Integer> cur = new ArrayList<>();
+
+    /**
+     * 递增子序列只是在子序列不重复的前提下，增加了递增条件
+     *
+     * @param nums
+     * @return
+     */
+    public List<List<Integer>> findSubsequences(int[] nums) {
+
+        boolean[] used = new boolean[nums.length];
+        backtracking(nums, 0, used);
+//        backtracking1(nums, 0);
+
+
+        return result;
+    }
+    //解答错误，但是没有发现具体是哪里发生了错误，答案好像也是对的;test2的后面的1111都不能加入答案
+    public void backtracking(int[] nums, int start, boolean[] used) {
+
+        if (start >= nums.length) {
+            return;
+        }
+
+        for (int i = start; i < nums.length; i++) {
+
+            if(i>start&&nums[i]<nums[i-1]){
+                return;
+            }
+
+            //同一树层不能使用相同的数字，TODO 这里的nums[i] == nums[i - 1]不行了，因为数组不能先排序，两个相同的数字不一定是连续的
+            if (i > start && nums[i] == nums[i - 1] && used[i - 1] == true) {
+                continue;
+            }
+            //要求单调递增才加入
+            if (cur.isEmpty() || cur.get(cur.size() - 1) <= nums[i]) {
+                cur.add(nums[i]);
+                used[i] = false;
+            } else {
+                continue;
+            }
+
+            if (cur.size() >= 2) {
+                result.add(new ArrayList<>(cur));
+            }
+
+            backtracking(nums, i+1, used);
+            //回溯
+            cur.remove(cur.size() - 1);
+            used[i] = true;
+
+
+        }
+    }
+
+    //代码随想录回溯:速度击败88.08%，内存击败98.92%
+    public void backtracking1(int[] nums, int start) {
+
+        if (cur.size()>1) {
+            result.add(new ArrayList<>(cur));
+            //注意这个不return，要取树上的节点
+        }
+
+        //记录同层数字是否用过
+        HashSet<Integer> set = new HashSet<>();
+        for (int i = start; i < nums.length; i++) {
+
+            //同一树层不能使用相同的数字
+
+            //要求单调递增才加入
+            if ((!cur.isEmpty() && cur.get(cur.size() - 1) > nums[i])||set.contains(nums[i])) {
+                continue;
+            }
+
+            set.add(nums[i]);
+            cur.add(nums[i]);
+
+            backtracking1(nums, i+1);
+            //回溯
+            cur.remove(cur.size() - 1);
+
+
+
+        }
+    }
+
+    //代码随想录回溯之Hash优化:由于题设数值范围为[-100,100]，完全可以使用数组来进行Hash优化
+    // 速度击败61.89%，内存击败28.23%
+    public void backtracking2(int[] nums, int start) {
+
+        if (cur.size()>1) {
+            result.add(new ArrayList<>(cur));
+            //注意这个不return，要取树上的节点
+        }
+
+        //记录同层数字是否用过
+        int[] used = new int[201];
+        for (int i = start; i < nums.length; i++) {
+
+            //同一树层不能使用相同的数字
+
+            //要求单调递增才加入
+            if ((!cur.isEmpty() && cur.get(cur.size() - 1) > nums[i])||used[nums[i]+100]==1) {
+                continue;
+            }
+
+            used[nums[i]+100]=1;//记录树层使用过，本层不能继续使用
+            cur.add(nums[i]);
+
+            backtracking2(nums, i+1);
+            //回溯
+            cur.remove(cur.size() - 1);
+
+
+
+        }
+    }
+
+
+    /**
+     * 官方的  二进制枚举+哈希法
+     */
+    List<Integer> temp = new ArrayList<Integer>();
+    List<List<Integer>> ans = new ArrayList<List<Integer>>();
+    Set<Integer> set = new HashSet<Integer>();
+    int n;
+
+    public List<List<Integer>> findSubsequences2(int[] nums) {
+        n = nums.length;
+        for (int i = 0; i < (1 << n); ++i) {
+            findSubsequences(i, nums);
+            int hashValue = getHash(263, (int) 1E9 + 7);
+            //使用监测hash值取去重，set中添加hash值
+            if (check() && !set.contains(hashValue)) {
+                ans.add(new ArrayList<Integer>(temp));
+                set.add(hashValue);
+            }
+        }
+        return ans;
+    }
+
+    public void findSubsequences(int mask, int[] nums) {
+        temp.clear();
+        for (int i = 0; i < n; ++i) {
+            if ((mask & 1) != 0) {
+                temp.add(nums[i]);
+            }
+            mask >>= 1;
+        }
+    }
+
+    public int getHash(int base, int mod) {
+        int hashValue = 0;
+        for (int x : temp) {
+            hashValue = hashValue * base % mod + (x + 101);
+            hashValue %= mod;
+        }
+        return hashValue;
+    }
+
+    public boolean check() {
+        for (int i = 1; i < temp.size(); ++i) {
+            if (temp.get(i) < temp.get(i - 1)) {
+                return false;
+            }
+        }
+        return temp.size() >= 2;
+    }
+
+
+}

Leecode/src/main/java/com/markilue/leecode/backtrace/Permute.java

@@ -0,0 +1,98 @@
+package com.markilue.leecode.backtrace;
+
+import org.junit.Test;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * @BelongsProject: Leecode
+ * @BelongsPackage: com.markilue.leecode.backtrace
+ * @Author: markilue
+ * @CreateTime: 2022-10-19  11:16
+ * @Description:
+ * TODO  leecode 46题  全排列：
+ *  给定一个不含重复数字的数组 nums ，返回其 所有可能的全排列 。你可以 按任意顺序 返回答案。
+ * @Version: 1.0
+ */
+public class Permute {
+
+    @Test
+    public void test(){
+        int[] nums = {1,2,3};
+        System.out.println(permute(nums));
+    }
+
+    @Test
+    public void test1(){
+        int[] nums = {0,1};
+        System.out.println(permute(nums));
+    }
+
+    @Test
+    public void test2(){
+        int[] nums = {0};
+        System.out.println(permute(nums));
+    }
+
+    /**
+     * 自己思路：全排列数组即数组中不包含该元素即可
+     * 速度击败81.77%，内存击败22.57%
+     * @param nums
+     * @return
+     */
+    public List<List<Integer>> permute(int[] nums) {
+
+
+//        backtracking(nums);
+        backtracking1(nums,new boolean[nums.length]);
+        return result;
+    }
+
+    List<List<Integer>> result = new ArrayList<>();
+    List<Integer> cur = new ArrayList<>();
+
+    public void backtracking(int[] nums){
+
+
+        if(cur.size()==nums.length){
+            result.add(new ArrayList<>(cur));
+            return;
+        }
+
+        for (int i = 0; i < nums.length; i++) {
+            if(cur.contains(nums[i])){
+                continue;
+            }
+            cur.add(nums[i]);
+            backtracking(nums);
+            cur.remove(cur.size()-1);
+        }
+
+
+    }
+
+
+    //代码随想录：使用used数组记录是否用过
+    //速度击败100%，内存击败38.03%
+    public void backtracking1(int[] nums,boolean[] used){
+
+        if(cur.size()==nums.length){
+            result.add(new ArrayList<>(cur));
+            return;
+        }
+
+        for (int i = 0; i < nums.length; i++) {
+
+            if(used[i]==true){
+                continue;
+            }
+            used[i]=true;
+            cur.add(nums[i]);
+            backtracking1(nums,used);
+            cur.remove(cur.size()-1);
+            used[i]=false;
+        }
+
+    }
+}

TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-C.py

@@ -472,19 +472,30 @@ def showResult(step_two_model: Joint_Monitoring, test_data, isPlot: bool = False
         total_result.append(output4)
     total_result = np.reshape(total_result, [total_result.__len__(), -1])
     total_result = np.reshape(total_result, [-1, ])
+
+    #误报率，漏报率，准确性的计算
+
+
     if isSave:
 
         np.savetxt(save_mse_name, total_result, delimiter=',')
     if isPlot:
+        plt.figure(1, figsize=(6.0, 2.68))
+        plt.subplots_adjust(left=0.1, right=0.94, bottom=0.2, top=0.9, wspace=None,
+                            hspace=None)
         plt.tight_layout()
+        font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 10}  # 设置坐标标签的字体大小，字体
+
         plt.scatter(list(range(total_result.shape[0])), total_result, c='black', s=10)
         # 画出 y=1 这条水平线
         plt.axhline(0.5, c='red', label='Failure threshold')
         # 箭头指向上面的水平线
         # plt.arrow(35000, 0.9, 33000, 0.75, head_width=0.02, head_length=0.1, shape="full", fc='red', ec='red',
         #           alpha=0.9, overhang=0.5)
-        # plt.text(35000, 0.9, "Truth Fault", fontsize=10, color='black', verticalalignment='top')
+        plt.text(test_data.shape[0] * 2 / 3+1000, 0.7, "Truth Fault", fontsize=10, color='red', verticalalignment='top')
         plt.axvline(test_data.shape[0] * 2 / 3, c='blue', ls='-.')
+        plt.xticks(range(6),('06/09/17','12/09/17','18/09/17','24/09/17','29/09/17'))  # 设置x轴的标尺
+        plt.tick_params()  #设置轴显示
         plt.xlabel("time",fontdict=font1)
         plt.ylabel("confience",fontdict=font1)
         plt.text(total_result.shape[0] * 4 / 5, 0.6, "Fault", fontsize=10, color='black', verticalalignment='top',

TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-MSE.py

@@ -7,4 +7,668 @@
 @Date : 2022/10/11 19:00
 @Usage : 
 @Desc :  使用MSE作为损失函数的RNet
-'''
+'''
+
+import tensorflow as tf
+import tensorflow.keras
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from model.DepthwiseCon1D.DepthwiseConv1D import DepthwiseConv1D
+from model.Dynamic_channelAttention.Dynamic_channelAttention import DynamicChannelAttention
+from condition_monitoring.data_deal import loadData
+from model.Joint_Monitoring.compare.RNet_MSE import Joint_Monitoring
+
+from model.CommonFunction.CommonFunction import *
+from sklearn.model_selection import train_test_split
+from tensorflow.keras.models import load_model, save_model
+import random
+
+'''超参数设置'''
+time_stamp = 120
+feature_num = 10
+batch_size = 16
+learning_rate = 0.001
+EPOCH = 101
+model_name = "RNet_MSE"
+'''EWMA超参数'''
+K = 18
+namuda = 0.01
+'''保存名称'''
+
+save_name = "./model/weight/{0}_weight/weight".format(model_name,
+                                                      time_stamp,
+                                                      feature_num,
+                                                      batch_size,
+                                                      EPOCH)
+save_step_two_name = "./model/two_weight/{0}_timestamp{1}_feature{2}_weight/weight".format(model_name,
+                                                                                           time_stamp,
+                                                                                           feature_num,
+                                                                                           batch_size,
+                                                                                           EPOCH)
+
+
+save_mse_name = "./mse/RNet_MSE/{0}_timestamp{1}_feature{2}_mse.csv".format(model_name,
+                                                                              time_stamp,
+                                                                              feature_num,
+                                                                              batch_size,
+                                                                              EPOCH)
+save_max_name = "./mse/RNet_MSE/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
+                                                                              time_stamp,
+                                                                              feature_num,
+                                                                             batch_size,
+                                                                              EPOCH)
+# save_name = "../model/joint/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+#                                                                    time_stamp,
+#                                                                    feature_num,
+#                                                                    batch_size,
+#                                                                    EPOCH)
+# save_step_two_name = "../model/joint_two/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+#                                                                                 time_stamp,
+#                                                                                 feature_num,
+#                                                                                 batch_size,
+#                                                                                 EPOCH)
+'''文件名'''
+file_name = "G:\data\SCADA数据\jb4q_8_delete_total_zero.csv"
+
+'''
+文件说明：jb4q_8_delete_total_zero.csv是删除了只删除了全是0的列的文件
+文件从0:415548行均是正常值(2019/7.30 00:00:00 - 2019/9/18  11:14:00)
+从415549:432153行均是异常值(2019/9/18  11:21:01 - 2021/1/18  00:00:00)
+'''
+'''文件参数'''
+# 最后正常的时间点
+healthy_date = 415548
+# 最后异常的时间点
+unhealthy_date = 432153
+# 异常容忍程度
+unhealthy_patience = 5
+
+
+def remove(data, time_stamp=time_stamp):
+    rows, cols = data.shape
+    print("remove_data.shape:", data.shape)
+    num = int(rows / time_stamp)
+
+    return data[:num * time_stamp, :]
+    pass
+
+
+# 不重叠采样
+def get_training_data(data, time_stamp: int = time_stamp):
+    removed_data = remove(data=data)
+    rows, cols = removed_data.shape
+    print("removed_data.shape:", data.shape)
+    print("removed_data:", removed_data)
+    train_data = np.reshape(removed_data, [-1, time_stamp, cols])
+    print("train_data:", train_data)
+    batchs, time_stamp, cols = train_data.shape
+
+    for i in range(1, batchs):
+        each_label = np.expand_dims(train_data[i, 0, :], axis=0)
+        if i == 1:
+            train_label = each_label
+        else:
+            train_label = np.concatenate([train_label, each_label], axis=0)
+
+    print("train_data.shape:", train_data.shape)
+    print("train_label.shape", train_label.shape)
+    return train_data[:-1, :], train_label
+
+
+# 重叠采样
+def get_training_data_overlapping(data, time_stamp: int = time_stamp, is_Healthy: bool = True):
+    rows, cols = data.shape
+    train_data = np.empty(shape=[rows - time_stamp - 1, time_stamp, cols])
+    train_label = np.empty(shape=[rows - time_stamp - 1, cols])
+    for i in range(rows):
+        if i + time_stamp >= rows:
+            break
+        if i + time_stamp < rows - 1:
+            train_data[i] = data[i:i + time_stamp]
+            train_label[i] = data[i + time_stamp]
+
+    print("重叠采样以后：")
+    print("data:", train_data)  # (300334,120,10)
+    print("label:", train_label)  # (300334,10)
+
+    if is_Healthy:
+        train_label2 = np.ones(shape=[train_label.shape[0]])
+    else:
+        train_label2 = np.zeros(shape=[train_label.shape[0]])
+
+    print("label2:", train_label2)
+
+    return train_data, train_label, train_label2
+
+
+# RepConv重参数化卷积
+def RepConv(input_tensor, k=3):
+    _, _, output_dim = input_tensor.shape
+    conv1 = tf.keras.layers.Conv1D(filters=output_dim, kernel_size=k, strides=1, padding='SAME')(input_tensor)
+    b1 = tf.keras.layers.BatchNormalization()(conv1)
+
+    conv2 = tf.keras.layers.Conv1D(filters=output_dim, kernel_size=1, strides=1, padding='SAME')(input_tensor)
+    b2 = tf.keras.layers.BatchNormalization()(conv2)
+
+    b3 = tf.keras.layers.BatchNormalization()(input_tensor)
+
+    out = tf.keras.layers.Add()([b1, b2, b3])
+    out = tf.nn.relu(out)
+    return out
+
+
+# RepBlock模块
+def RepBlock(input_tensor, num: int = 3):
+    for i in range(num):
+        input_tensor = RepConv(input_tensor)
+    return input_tensor
+
+
+# GAP 全局平均池化
+def Global_avg_channelAttention(input_tensor):
+    _, length, channel = input_tensor.shape
+    DWC1 = DepthwiseConv1D(kernel_size=1, padding='SAME')(input_tensor)
+    GAP = tf.keras.layers.GlobalAvgPool1D()(DWC1)
+    c1 = tf.keras.layers.Conv1D(filters=channel, kernel_size=1, padding='SAME')(GAP)
+    s1 = tf.nn.sigmoid(c1)
+    output = tf.multiply(input_tensor, s1)
+    return output
+
+
+# GDP 全局动态池化
+def Global_Dynamic_channelAttention(input_tensor):
+    _, length, channel = input_tensor.shape
+    DWC1 = DepthwiseConv1D(kernel_size=1, padding='SAME')(input_tensor)
+
+    # GAP
+    GAP = tf.keras.layers.GlobalAvgPool1D()(DWC1)
+    c1 = tf.keras.layers.Conv1D(filters=channel, kernel_size=1, padding='SAME')(GAP)
+    s1 = tf.nn.sigmoid(c1)
+
+    # GMP
+    GMP = tf.keras.layers.GlobalMaxPool1D()(DWC1)
+    c2 = tf.keras.layers.Conv1D(filters=channel, kernel_size=1, padding='SAME')(GMP)
+    s3 = tf.nn.sigmoid(c2)
+
+    output = tf.multiply(input_tensor, s1)
+    return output
+
+
+# 归一化
+def normalization(data):
+    rows, cols = data.shape
+    print("归一化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 归一化
+    max = np.max(data, axis=0)
+    max = np.broadcast_to(max, [rows, cols])
+    min = np.min(data, axis=0)
+    min = np.broadcast_to(min, [rows, cols])
+
+    data = (data - min) / (max - min)
+    print("归一化之后:", data)
+    print(data.shape)
+
+    return data
+
+
+# 正则化
+def Regularization(data):
+    rows, cols = data.shape
+    print("正则化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 正则化
+    mean = np.mean(data, axis=0)
+    mean = np.broadcast_to(mean, shape=[rows, cols])
+    dst = np.sqrt(np.var(data, axis=0))
+    dst = np.broadcast_to(dst, shape=[rows, cols])
+    data = (data - mean) / dst
+    print("正则化之后:", data)
+    print(data.shape)
+
+    return data
+    pass
+
+
+def EWMA(data, K=K, namuda=namuda):
+    # t是啥暂时未知
+    t = 0
+    mid = np.mean(data, axis=0)
+    standard = np.sqrt(np.var(data, axis=0))
+    UCL = mid + K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    LCL = mid - K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    return mid, UCL, LCL
+    pass
+
+
+# trian_data:(300455,120,10)
+# trian_label1:(300455,10)
+# trian_label2:(300455,)
+def shuffle(train_data, train_label1, train_label2, is_split: bool = False, split_size: float = 0.2):
+    (train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(train_data,
+                                                                                                     train_label1,
+                                                                                                     train_label2,
+                                                                                                     test_size=split_size,
+                                                                                                     shuffle=True,
+                                                                                                     random_state=100)
+    if is_split:
+        return train_data, train_label1, train_label2, test_data, test_label1, test_label2
+    train_data = np.concatenate([train_data, test_data], axis=0)
+    train_label1 = np.concatenate([train_label1, test_label1], axis=0)
+    train_label2 = np.concatenate([train_label2, test_label2], axis=0)
+    # print(train_data.shape)
+    # print(train_label1.shape)
+    # print(train_label2.shape)
+    # print(train_data.shape)
+
+    return train_data, train_label1, train_label2
+    pass
+
+
+def split_test_data(healthy_data, healthy_label1, healthy_label2, unhealthy_data, unhealthy_label1, unhealthy_label2,
+                    split_size: float = 0.2, shuffle: bool = True):
+    data = np.concatenate([healthy_data, unhealthy_data], axis=0)
+    label1 = np.concatenate([healthy_label1, unhealthy_label1], axis=0)
+    label2 = np.concatenate([healthy_label2, unhealthy_label2], axis=0)
+    (train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(data,
+                                                                                                     label1,
+                                                                                                     label2,
+                                                                                                     test_size=split_size,
+                                                                                                     shuffle=shuffle,
+                                                                                                     random_state=100)
+
+    # print(train_data.shape)
+    # print(train_label1.shape)
+    # print(train_label2.shape)
+    # print(train_data.shape)
+
+    return train_data, train_label1, train_label2, test_data, test_label1, test_label2
+
+    pass
+
+
+# trian_data:(300455,120,10)
+# trian_label1:(300455,10)
+# trian_label2:(300455,)
+def train_step_one(train_data, train_label1, train_label2):
+    model = Joint_Monitoring()
+    # # # # TODO 需要运行编译一次,才能打印model.summary()
+    # model.build(input_shape=(batch_size, filter_num, dims))
+    # model.summary()
+    history_loss = []
+    history_val_loss = []
+    learning_rate = 1e-3
+    for epoch in range(EPOCH):
+
+        print()
+        print("EPOCH:", epoch, "/", EPOCH, ":")
+        train_data, train_label1, train_label2 = shuffle(train_data, train_label1, train_label2)
+        if epoch == 0:
+            train_data, train_label1, train_label2, val_data, val_label1, val_label2 = shuffle(train_data, train_label1,
+                                                                                               train_label2,
+                                                                                               is_split=True)
+        # print()
+        # print("EPOCH:", epoch, "/", EPOCH, ":")
+        # 用于让train知道，这是这个epoch中的第几次训练
+        z = 0
+        # 用于batch_size次再训练
+        k = 1
+        for data_1, label_1, label_2 in zip(train_data, train_label1, train_label2):
+            size, _, _ = train_data.shape
+            data_1 = tf.expand_dims(data_1, axis=0)
+            label_1 = tf.expand_dims(label_1, axis=0)
+            label_2 = tf.expand_dims(label_2, axis=0)
+            if batch_size != 1:
+                if k % batch_size == 1:
+                    data = data_1
+                    label1 = label_1
+                    label2 = label_2
+                else:
+                    data = tf.concat([data, data_1], axis=0)
+                    label1 = tf.concat([label1, label_1], axis=0)
+                    label2 = tf.concat([label2, label_2], axis=0)
+            else:
+                data = data_1
+                label1 = label_1
+                label2 = label_2
+
+            if k % batch_size == 0:
+                # label = tf.expand_dims(label, axis=-1)
+                loss_value, accuracy_value = model.train(input_tensor=data, label1=label1, label2=label2,
+                                                         learning_rate=learning_rate,
+                                                         is_first_time=True)
+                print(z * batch_size, "/", size, ":===============>", "loss:", loss_value.numpy())
+                k = 0
+                z = z + 1
+            k = k + 1
+        val_loss, val_accuracy = model.get_val_loss(val_data=val_data, val_label1=val_label1, val_label2=val_label2,
+                                                    is_first_time=True)
+        SaveBestModel(model=model, save_name=save_name, history_loss=history_val_loss, loss_value=val_loss.numpy())
+        # SaveBestH5Model(model=model, save_name=save_name, history_loss=history_val_loss, loss_value=val_loss.numpy())
+        history_val_loss.append(val_loss)
+        history_loss.append(loss_value.numpy())
+        print('Training loss is :', loss_value.numpy())
+        print('Validating loss is :', val_loss.numpy())
+        if IsStopTraining(history_loss=history_val_loss, patience=7):
+            break
+        if Is_Reduce_learning_rate(history_loss=history_val_loss, patience=3):
+            if learning_rate >= 1e-4:
+                learning_rate = learning_rate * 0.1
+    pass
+
+
+def train_step_two(step_one_model, step_two_model, train_data, train_label1, train_label2):
+    # step_two_model = Joint_Monitoring()
+    # step_two_model.build(input_shape=(batch_size, time_stamp, feature_num))
+    # step_two_model.summary()
+    history_loss = []
+    history_val_loss = []
+    history_accuracy = []
+    learning_rate = 1e-3
+    for epoch in range(EPOCH):
+        print()
+        print("EPOCH:", epoch, "/", EPOCH, ":")
+        train_data, train_label1, train_label2 = shuffle(train_data, train_label1, train_label2)
+        if epoch == 0:
+            train_data, train_label1, train_label2, val_data, val_label1, val_label2 = shuffle(train_data, train_label1,
+                                                                                               train_label2,
+                                                                                               is_split=True)
+        # print()
+        # print("EPOCH:", epoch, "/", EPOCH, ":")
+        # 用于让train知道，这是这个epoch中的第几次训练
+        z = 0
+        # 用于batch_size次再训练
+        k = 1
+        accuracy_num = 0
+        for data_1, label_1, label_2 in zip(train_data, train_label1, train_label2):
+            size, _, _ = train_data.shape
+            data_1 = tf.expand_dims(data_1, axis=0)
+            label_1 = tf.expand_dims(label_1, axis=0)
+            label_2 = tf.expand_dims(label_2, axis=0)
+            if batch_size != 1:
+                if k % batch_size == 1:
+                    data = data_1
+                    label1 = label_1
+                    label2 = label_2
+                else:
+                    data = tf.concat([data, data_1], axis=0)
+                    label1 = tf.concat([label1, label_1], axis=0)
+                    label2 = tf.concat([label2, label_2], axis=0)
+            else:
+                data = data_1
+                label1 = label_1
+                label2 = label_2
+
+            if k % batch_size == 0:
+                # label = tf.expand_dims(label, axis=-1)
+                output1, output2, output3, _ = step_one_model.call(inputs=data, is_first_time=True)
+                loss_value, accuracy_value = step_two_model.train(input_tensor=data, label1=label1, label2=label2,
+                                                                  learning_rate=learning_rate,
+                                                                  is_first_time=False, pred_3=output1, pred_4=output2,
+                                                                  pred_5=output3)
+                accuracy_num += accuracy_value
+                print(z * batch_size, "/", size, ":===============>", "loss:", loss_value.numpy(), "| accuracy:",
+                      accuracy_num / ((z + 1) * batch_size))
+                k = 0
+                z = z + 1
+            k = k + 1
+
+        val_loss, val_accuracy = step_two_model.get_val_loss(val_data=val_data, val_label1=val_label1,
+                                                             val_label2=val_label2,
+                                                             is_first_time=False, step_one_model=step_one_model)
+        SaveBestModelByAccuracy(model=step_two_model, save_name=save_step_two_name, history_accuracy=history_accuracy,
+                                accuracy_value=val_accuracy)
+        history_val_loss.append(val_loss)
+        history_loss.append(loss_value.numpy())
+        history_accuracy.append(val_accuracy)
+        print('Training loss is : {0} | Training accuracy is : {1}'.format(loss_value.numpy(),
+                                                                           accuracy_num / ((z + 1) * batch_size)))
+        print('Validating loss is : {0} | Validating accuracy is : {1}'.format(val_loss.numpy(), val_accuracy))
+        if IsStopTraining(history_loss=history_val_loss, patience=7):
+            break
+        if Is_Reduce_learning_rate(history_loss=history_val_loss, patience=3):
+            if learning_rate >= 1e-4:
+                learning_rate = learning_rate * 0.1
+    pass
+
+
+def test(step_one_model, step_two_model, test_data, test_label1, test_label2):
+    history_loss = []
+    history_val_loss = []
+
+    val_loss, val_accuracy = step_two_model.get_val_loss(val_data=test_data, val_label1=test_label1,
+                                                         val_label2=test_label2,
+                                                         is_first_time=False, step_one_model=step_one_model)
+
+    history_val_loss.append(val_loss)
+    print("val_accuracy:", val_accuracy)
+    print("val_loss:", val_loss)
+
+
+def showResult(step_two_model: Joint_Monitoring, test_data, isPlot: bool = False):
+    # 获取模型的所有参数的个数
+    # step_two_model.count_params()
+    total_result = []
+    size, length, dims = test_data.shape
+    for epoch in range(0, size - batch_size + 1, batch_size):
+        each_test_data = test_data[epoch:epoch + batch_size, :, :]
+        _, _, _, output4 = step_two_model.call(each_test_data, is_first_time=False)
+        total_result.append(output4)
+    total_result = np.reshape(total_result, [total_result.__len__(), -1])
+    total_result = np.reshape(total_result, [-1, ])
+    if isPlot:
+        plt.scatter(list(range(total_result.shape[0])), total_result, c='black', s=10)
+        # 画出 y=1 这条水平线
+        plt.axhline(0.5, c='red', label='Failure threshold')
+        # 箭头指向上面的水平线
+        # plt.arrow(35000, 0.9, 33000, 0.75, head_width=0.02, head_length=0.1, shape="full", fc='red', ec='red',
+        #           alpha=0.9, overhang=0.5)
+        # plt.text(35000, 0.9, "Truth Fault", fontsize=10, color='black', verticalalignment='top')
+        plt.axvline(test_data.shape[0] * 2 / 3, c='blue', ls='-.')
+        plt.xlabel("time")
+        plt.ylabel("confience")
+        plt.text(total_result.shape[0] * 4 / 5, 0.6, "Fault", fontsize=10, color='black', verticalalignment='top',
+                 horizontalalignment='center',
+                 bbox={'facecolor': 'grey',
+                       'pad': 10})
+        plt.text(total_result.shape[0] * 1 / 3, 0.4, "Norm", fontsize=10, color='black', verticalalignment='top',
+                 horizontalalignment='center',
+                 bbox={'facecolor': 'grey',
+                       'pad': 10})
+        plt.grid()
+        # plt.ylim(0, 1)
+        # plt.xlim(-50, 1300)
+        # plt.legend("", loc='upper left')
+        plt.show()
+    return total_result
+
+
+def get_MSE(data, label, new_model, isStandard: bool = True, isPlot: bool = True, predictI: int = 1):
+    predicted_data1 = []
+    predicted_data2 = []
+    predicted_data3 = []
+    size, length, dims = data.shape
+    for epoch in range(0, size, batch_size):
+        each_test_data = data[epoch:epoch + batch_size, :, :]
+        output1, output2, output3, _ = new_model.call(inputs=each_test_data, is_first_time=True)
+        if epoch == 0:
+            predicted_data1 = output1
+            predicted_data2 = output2
+            predicted_data3 = output3
+        else:
+            predicted_data1 = np.concatenate([predicted_data1, output1], axis=0)
+            predicted_data2 = np.concatenate([predicted_data2, output2], axis=0)
+            predicted_data3 = np.concatenate([predicted_data3, output3], axis=0)
+
+    predicted_data1 = np.reshape(predicted_data1, [-1, 10])
+    predicted_data2 = np.reshape(predicted_data2, [-1, 10])
+    predicted_data3 = np.reshape(predicted_data3, [-1, 10])
+    predict_data = 0
+
+    predict_data = predicted_data1
+    mseList = []
+    meanList = []
+    maxList = []
+
+    for i in range(1, 4):
+        print("i:", i)
+        if i == 1:
+            predict_data = predicted_data1
+        elif i == 2:
+            predict_data = predicted_data2
+        elif i == 3:
+            predict_data = predicted_data3
+        temp = np.abs(predict_data - label)
+        temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predict_data.shape))
+        temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predict_data.shape)
+        temp3 = temp1 / temp2
+        mse = np.sum((temp1 / temp2) ** 2, axis=1)
+
+        print("mse.shape:", mse.shape)
+        # mse=np.mean((predicted_data-label)**2,axis=1)
+        # print("mse", mse)
+        mseList.append(mse)
+        if isStandard:
+            dims, = mse.shape
+            mean = np.mean(mse)
+            std = np.sqrt(np.var(mse))
+            max = mean + 3 * std
+            print("max.shape:", max.shape)
+            # min = mean-3*std
+            max = np.broadcast_to(max, shape=[dims, ])
+            # min = np.broadcast_to(min,shape=[dims,])
+            mean = np.broadcast_to(mean, shape=[dims, ])
+            if isPlot:
+                plt.figure(random.randint(1, 100))
+                plt.plot(max)
+                plt.plot(mse)
+                plt.plot(mean)
+                # plt.plot(min)
+                plt.show()
+            maxList.append(max)
+            meanList.append(mean)
+        else:
+            if isPlot:
+                plt.figure(random.randint(1, 100))
+                plt.plot(mse)
+                # plt.plot(min)
+                plt.show()
+
+    return mseList, meanList, maxList
+    # pass
+
+
+# healthy_data是健康数据,用于确定阈值，all_data是完整的数据,用于模型出结果
+def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data, unhealthy_label, isPlot: bool = False,
+              isSave: bool = False, predictI: int = 1):
+    # TODO 计算MSE确定阈值
+    # plt.ion()
+    mseList, meanList, maxList = get_MSE(healthy_data, healthy_label, model)
+    mse1List, _, _ = get_MSE(unhealthy_data, unhealthy_label, model, isStandard=False)
+
+    for mse, mean, max, mse1,j in zip(mseList, meanList, maxList, mse1List,range(3)):
+
+        # 误报率的计算
+        total, = mse.shape
+        faultNum = 0
+        faultList = []
+        for i in range(total):
+            if (mse[i] > max[i]):
+                faultNum += 1
+                faultList.append(mse[i])
+
+        fault_rate = faultNum / total
+        print("误报率:", fault_rate)
+
+        # 漏报率计算
+        missNum = 0
+        missList = []
+        all, = mse1.shape
+        for i in range(all):
+            if (mse1[i] < max[0]):
+                missNum += 1
+                missList.append(mse1[i])
+
+        miss_rate = missNum / all
+        print("漏报率:", miss_rate)
+
+        # 总体图
+        print("mse:", mse)
+        print("mse1:", mse1)
+        print("============================================")
+        total_mse = np.concatenate([mse, mse1], axis=0)
+        total_max = np.broadcast_to(max[0], shape=[total_mse.shape[0], ])
+        # min = np.broadcast_to(min,shape=[dims,])
+        total_mean = np.broadcast_to(mean[0], shape=[total_mse.shape[0], ])
+
+        if isSave:
+            save_mse_name1=save_mse_name[:-4]+"_predict"+str(j+1)+".csv"
+            save_max_name1=save_max_name[:-4]+"_predict"+str(j+1)+".csv"
+
+            np.savetxt(save_mse_name1,total_mse, delimiter=',')
+            np.savetxt(save_max_name1,total_max, delimiter=',')
+
+
+        plt.figure(random.randint(1, 100))
+        plt.plot(total_max)
+        plt.plot(total_mse)
+        plt.plot(total_mean)
+        # plt.plot(min)
+        plt.show()
+    pass
+
+
+if __name__ == '__main__':
+    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data = normalization(data=total_data)
+    train_data_healthy, train_label1_healthy, train_label2_healthy = get_training_data_overlapping(
+        total_data[:healthy_date, :], is_Healthy=True)
+    train_data_unhealthy, train_label1_unhealthy, train_label2_unhealthy = get_training_data_overlapping(
+        total_data[healthy_date - time_stamp + unhealthy_patience:unhealthy_date, :],
+        is_Healthy=False)
+    #### TODO 第一步训练
+    # 单次测试
+    # train_step_one(train_data=train_data_healthy[:256, :, :], train_label1=train_label1_healthy[:256, :],
+    #                train_label2=train_label2_healthy[:256, ])
+    ####  模型训练
+    train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy)
+
+    # 导入第一步已经训练好的模型,一个继续训练，一个只输出结果
+    step_one_model = Joint_Monitoring()
+    step_one_model.load_weights(save_name)
+    #
+    # step_two_model = Joint_Monitoring()
+    # step_two_model.load_weights(save_name)
+
+    # #### TODO 计算MSE
+    healthy_size, _, _ = train_data_healthy.shape
+    unhealthy_size, _, _ = train_data_unhealthy.shape
+    all_data, _, _ = get_training_data_overlapping(
+        total_data[healthy_size - 2 * unhealthy_size:unhealthy_date, :], is_Healthy=True)
+
+    ##出结果单次测试
+    # getResult(step_one_model,
+    #           healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:healthy_size - 2 * unhealthy_size + 200,
+    #                        :],
+    #           healthy_label=train_label1_healthy[
+    #                         healthy_size - 2 * unhealthy_size:healthy_size - 2 * unhealthy_size + 200, :],
+    #           unhealthy_data=train_data_unhealthy[:200, :], unhealthy_label=train_label1_unhealthy[:200, :],isSave=True)
+
+    getResult(step_one_model, healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:, :],
+              healthy_label=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
+              unhealthy_data=train_data_unhealthy, unhealthy_label=train_label1_unhealthy, isSave=True)
+
+    ###TODO 展示全部的结果
+    # all_data, _, _ = get_training_data_overlapping(
+    #     total_data[healthy_size - 2 * unhealthy_size:unhealthy_date, :], is_Healthy=True)
+    # all_data = np.concatenate([])
+    # 单次测试
+    # showResult(step_two_model, test_data=all_data[:32], isPlot=True)
+    # showResult(step_two_model, test_data=all_data, isPlot=True)
+
+    pass

TensorFlow_eaxmple/Model_train_test/model/Joint_Monitoring/compare/RNet_MSE.py

@@ -72,40 +72,40 @@ class Joint_Monitoring(keras.Model):
         RepDCBlock1 = tf.keras.layers.BatchNormalization()(RepDCBlock1)
         conv1 = self.conv1(RepDCBlock1)
         conv1 = tf.nn.leaky_relu(conv1)
-        conv1 = tf.keras.layers.BatchNormalization()(conv1)
+        # conv1 = tf.keras.layers.BatchNormalization()(conv1)
         upsample1 = self.upsample1(conv1)
 
         # DACU2 = self.DACU2(upsample1)
-        DACU2 = tf.keras.layers.BatchNormalization()(upsample1)
+        # DACU2 = tf.keras.layers.BatchNormalization()(upsample1)
-        RepDCBlock2 = self.RepDCBlock2(DACU2)
+        RepDCBlock2 = self.RepDCBlock2(upsample1)
-        RepDCBlock2 = tf.keras.layers.BatchNormalization()(RepDCBlock2)
+        # RepDCBlock2 = tf.keras.layers.BatchNormalization()(RepDCBlock2)
         conv2 = self.conv2(RepDCBlock2)
         conv2 = tf.nn.leaky_relu(conv2)
-        conv2 = tf.keras.layers.BatchNormalization()(conv2)
+        # conv2 = tf.keras.layers.BatchNormalization()(conv2)
         upsample2 = self.upsample2(conv2)
 
         # DACU3 = self.DACU3(upsample2)
-        DACU3 = tf.keras.layers.BatchNormalization()(upsample2)
+        # DACU3 = tf.keras.layers.BatchNormalization()(upsample2)
-        RepDCBlock3 = self.RepDCBlock3(DACU3)
+        RepDCBlock3 = self.RepDCBlock3(upsample2)
-        RepDCBlock3 = tf.keras.layers.BatchNormalization()(RepDCBlock3)
+        # RepDCBlock3 = tf.keras.layers.BatchNormalization()(RepDCBlock3)
         conv3 = self.conv3(RepDCBlock3)
         conv3 = tf.nn.leaky_relu(conv3)
-        conv3 = tf.keras.layers.BatchNormalization()(conv3)
+        # conv3 = tf.keras.layers.BatchNormalization()(conv3)
 
         concat1 = tf.concat([conv2, conv3], axis=1)
 
         # DACU4 = self.DACU4(concat1)
-        DACU4 = tf.keras.layers.BatchNormalization()(concat1)
+        # DACU4 = tf.keras.layers.BatchNormalization()(concat1)
-        RepDCBlock4 = self.RepDCBlock4(DACU4)
+        RepDCBlock4 = self.RepDCBlock4(concat1)
-        RepDCBlock4 = tf.keras.layers.BatchNormalization()(RepDCBlock4)
+        # RepDCBlock4 = tf.keras.layers.BatchNormalization()(RepDCBlock4)
         conv4 = self.conv4(RepDCBlock4)
         conv4 = tf.nn.leaky_relu(conv4)
-        conv4 = tf.keras.layers.BatchNormalization()(conv4)
+        # conv4 = tf.keras.layers.BatchNormalization()(conv4)
 
         concat2 = tf.concat([conv1, conv4], axis=1)
 
         RepDCBlock5 = self.RepDCBlock5(concat2)
-        RepDCBlock5 = tf.keras.layers.BatchNormalization()(RepDCBlock5)
+        # RepDCBlock5 = tf.keras.layers.BatchNormalization()(RepDCBlock5)
 
         output1 = []
         output2 = []
@@ -117,25 +117,25 @@ class Joint_Monitoring(keras.Model):
             # 重现原数据
             # 接block3
             GRU1 = self.GRU1(RepDCBlock3)
-            GRU1 = tf.keras.layers.BatchNormalization()(GRU1)
+            # GRU1 = tf.keras.layers.BatchNormalization()(GRU1)
             d1 = self.d1(GRU1)
             # tf.nn.softmax
             output1 = self.output1(d1)
             # 接block4
             GRU2 = self.GRU2(RepDCBlock4)
-            GRU2 = tf.keras.layers.BatchNormalization()(GRU2)
+            # GRU2 = tf.keras.layers.BatchNormalization()(GRU2)
             d2 = self.d2(GRU2)
             # tf.nn.softmax
             output2 = self.output2(d2)
             # 接block5
             GRU3 = self.GRU3(RepDCBlock5)
-            GRU3 = tf.keras.layers.BatchNormalization()(GRU3)
+            # GRU3 = tf.keras.layers.BatchNormalization()(GRU3)
             d3 = self.d3(GRU3)
             # tf.nn.softmax
             output3 = self.output3(d3)
         else:
             GRU1 = self.GRU1(RepDCBlock3)
-            GRU1 = tf.keras.layers.BatchNormalization()(GRU1)
+            # GRU1 = tf.keras.layers.BatchNormalization()(GRU1)
             d1 = self.d1(GRU1)
             # tf.nn.softmax
             output1 = self.output1(d1)
@@ -147,7 +147,7 @@ class Joint_Monitoring(keras.Model):
             output2 = self.output2(d2)
             # 接block5
             GRU3 = self.GRU3(RepDCBlock5)
-            GRU3 = tf.keras.layers.BatchNormalization()(GRU3)
+            # GRU3 = tf.keras.layers.BatchNormalization()(GRU3)
             d3 = self.d3(GRU3)
             # tf.nn.softmax
             output3 = self.output3(d3)
@@ -175,73 +175,73 @@ class Joint_Monitoring(keras.Model):
                  pred_5=None):
         # step one
         RepDCBlock1 = self.RepDCBlock1(inputs_tensor)
-        RepDCBlock1 = tf.keras.layers.BatchNormalization()(RepDCBlock1)
+        # RepDCBlock1 = tf.keras.layers.BatchNormalization()(RepDCBlock1)
         conv1 = self.conv1(RepDCBlock1)
         conv1 = tf.nn.leaky_relu(conv1)
-        conv1 = tf.keras.layers.BatchNormalization()(conv1)
+        # conv1 = tf.keras.layers.BatchNormalization()(conv1)
         upsample1 = self.upsample1(conv1)
 
         # DACU2 = self.DACU2(upsample1)
-        DACU2 = tf.keras.layers.BatchNormalization()(upsample1)
+        # DACU2 = tf.keras.layers.BatchNormalization()(upsample1)
-        RepDCBlock2 = self.RepDCBlock2(DACU2)
+        RepDCBlock2 = self.RepDCBlock2(upsample1)
-        RepDCBlock2 = tf.keras.layers.BatchNormalization()(RepDCBlock2)
+        # RepDCBlock2 = tf.keras.layers.BatchNormalization()(RepDCBlock2)
         conv2 = self.conv2(RepDCBlock2)
         conv2 = tf.nn.leaky_relu(conv2)
-        conv2 = tf.keras.layers.BatchNormalization()(conv2)
+        # conv2 = tf.keras.layers.BatchNormalization()(conv2)
         upsample2 = self.upsample2(conv2)
 
         # DACU3 = self.DACU3(upsample2)
-        DACU3 = tf.keras.layers.BatchNormalization()(upsample2)
+        # DACU3 = tf.keras.layers.BatchNormalization()(upsample2)
-        RepDCBlock3 = self.RepDCBlock3(DACU3)
+        RepDCBlock3 = self.RepDCBlock3(upsample2)
-        RepDCBlock3 = tf.keras.layers.BatchNormalization()(RepDCBlock3)
+        # RepDCBlock3 = tf.keras.layers.BatchNormalization()(RepDCBlock3)
         conv3 = self.conv3(RepDCBlock3)
         conv3 = tf.nn.leaky_relu(conv3)
-        conv3 = tf.keras.layers.BatchNormalization()(conv3)
+        # conv3 = tf.keras.layers.BatchNormalization()(conv3)
 
         concat1 = tf.concat([conv2, conv3], axis=1)
 
         # DACU4 = self.DACU4(concat1)
-        DACU4 = tf.keras.layers.BatchNormalization()(concat1)
+        # DACU4 = tf.keras.layers.BatchNormalization()(concat1)
-        RepDCBlock4 = self.RepDCBlock4(DACU4)
+        RepDCBlock4 = self.RepDCBlock4(concat1)
-        RepDCBlock4 = tf.keras.layers.BatchNormalization()(RepDCBlock4)
+        # RepDCBlock4 = tf.keras.layers.BatchNormalization()(RepDCBlock4)
         conv4 = self.conv4(RepDCBlock4)
         conv4 = tf.nn.leaky_relu(conv4)
-        conv4 = tf.keras.layers.BatchNormalization()(conv4)
+        # conv4 = tf.keras.layers.BatchNormalization()(conv4)
 
         concat2 = tf.concat([conv1, conv4], axis=1)
 
         RepDCBlock5 = self.RepDCBlock5(concat2)
-        RepDCBlock5 = tf.keras.layers.BatchNormalization()(RepDCBlock5)
+        # RepDCBlock5 = tf.keras.layers.BatchNormalization()(RepDCBlock5)
 
         if is_first_time:
             # step two
             # 重现原数据
             # 接block3
             GRU1 = self.GRU1(RepDCBlock3)
-            GRU1 = tf.keras.layers.BatchNormalization()(GRU1)
+            # GRU1 = tf.keras.layers.BatchNormalization()(GRU1)
             d1 = self.d1(GRU1)
             # tf.nn.softmax
             output1 = self.output1(d1)
             # 接block4
             GRU2 = self.GRU2(RepDCBlock4)
-            GRU2 = tf.keras.layers.BatchNormalization()(GRU2)
+            # GRU2 = tf.keras.layers.BatchNormalization()(GRU2)
             d2 = self.d2(GRU2)
             # tf.nn.softmax
             output2 = self.output2(d2)
             # 接block5
             GRU3 = self.GRU3(RepDCBlock5)
-            GRU3 = tf.keras.layers.BatchNormalization()(GRU3)
+            # GRU3 = tf.keras.layers.BatchNormalization()(GRU3)
             d3 = self.d3(GRU3)
             # tf.nn.softmax
             output3 = self.output3(d3)
 
             # reduce_mean降维计算均值
-            MSE_loss1 = SmoothL1Loss()(y_true=label1, y_pred=output1)
+            # MSE_loss1 = SmoothL1Loss()(y_true=label1, y_pred=output1)
-            MSE_loss2 = SmoothL1Loss()(y_true=label1, y_pred=output2)
+            # MSE_loss2 = SmoothL1Loss()(y_true=label1, y_pred=output2)
-            MSE_loss3 = SmoothL1Loss()(y_true=label1, y_pred=output3)
+            # MSE_loss3 = SmoothL1Loss()(y_true=label1, y_pred=output3)
-            # MSE_loss1 = tf.reduce_mean(tf.keras.losses.mse(y_true=label1, y_pred=output1))
+            MSE_loss1 = tf.reduce_mean(tf.keras.losses.mse(y_true=label1, y_pred=output1))
-            # MSE_loss2 = tf.reduce_mean(tf.keras.losses.mse(y_true=label1, y_pred=output2))
+            MSE_loss2 = tf.reduce_mean(tf.keras.losses.mse(y_true=label1, y_pred=output2))
-            # MSE_loss3 = tf.reduce_mean(tf.keras.losses.mse(y_true=label1, y_pred=output3))
+            MSE_loss3 = tf.reduce_mean(tf.keras.losses.mse(y_true=label1, y_pred=output3))
 
             print("MSE_loss1:", MSE_loss1.numpy())
             print("MSE_loss2:", MSE_loss2.numpy())
@@ -254,19 +254,19 @@ class Joint_Monitoring(keras.Model):
             # 重现原数据
             # 接block3
             GRU1 = self.GRU1(RepDCBlock3)
-            GRU1 = tf.keras.layers.BatchNormalization()(GRU1)
+            # GRU1 = tf.keras.layers.BatchNormalization()(GRU1)
             d1 = self.d1(GRU1)
             # tf.nn.softmax
             output1 = self.output1(d1)
             # 接block4
             GRU2 = self.GRU2(RepDCBlock4)
-            GRU2 = tf.keras.layers.BatchNormalization()(GRU2)
+            # GRU2 = tf.keras.layers.BatchNormalization()(GRU2)
             d2 = self.d2(GRU2)
             # tf.nn.softmax
             output2 = self.output2(d2)
             # 接block5
             GRU3 = self.GRU3(RepDCBlock5)
-            GRU3 = tf.keras.layers.BatchNormalization()(GRU3)
+            # GRU3 = tf.keras.layers.BatchNormalization()(GRU3)
             d3 = self.d3(GRU3)
             # tf.nn.softmax
             output3 = self.output3(d3)