leecode更新

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

@@ -0,0 +1,168 @@
+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: 2022-10-17  10:25
+ * @Description: TODO  力扣93题   复原IP地址：
+ * 有效 IP 地址 正好由四个整数（每个整数位于 0 到 255 之间组成，且不能含有前导 0），整数之间用 '.' 分隔。
+ * <p>
+ * 例如："0.1.2.201" 和 "192.168.1.1" 是 有效 IP 地址，但是 "0.011.255.245"、"192.168.1.312" 和 "192.168@1.1" 是 无效 IP 地址。
+ * 给定一个只包含数字的字符串 s ，用以表示一个 IP 地址，返回所有可能的有效 IP 地址，这些地址可以通过在 s 中插入 '.' 来形成。你 不能 重新排序或删除 s 中的任何数字。你可以按 任何 顺序返回答案。
+ * @Version: 1.0
+ */
+public class RestoreIpAddresses {
+
+    @Test
+    public void test1() {
+        String s = "25525511135";
+        System.out.println(restoreIpAddresses(s));
+    }
+
+    @Test
+    public void test2() {
+        String s = "0000";
+        System.out.println(restoreIpAddresses(s));
+    }
+
+    @Test
+    public void test3() {
+        String s = "101023";
+        System.out.println(restoreIpAddresses(s));
+    }
+
+
+    @Test
+    public void test4() {
+        String s = "0279245587303";
+        System.out.println(restoreIpAddresses(s));
+    }
+
+    /**
+     * 回溯算法：与partition切割回文有点像，就是切割完的判断条件不一样
+     *  速度击败95.41%，内存击败43.81%
+     * @param s
+     * @return
+     */
+    public List<String> restoreIpAddresses(String s) {
+        if(s.length()>12){
+            return result;
+        }
+        backtracking(s, 0, 0);
+        return result;
+
+    }
+
+    List<String> result = new ArrayList<>();
+    StringBuilder builder = new StringBuilder();
+
+    public void backtracking(String s, int start, int num) {
+
+//        if (start >= s.length()) {
+//            result.add(builder.toString());
+//            return;
+//        }
+
+        if (num == 3) {
+            if (valid(s, start, s.length())) {
+                String substring = s.substring(start, s.length());
+                builder.append(substring);
+                result.add(builder.toString());
+                builder.delete(builder.length()-substring.length(),builder.length());
+                return;
+            }
+            return;
+        }
+
+        for (int i = start+1; i < s.length() && i < start + 4; i++) {
+            if (valid(s, start,  i)) {
+                builder.append(s.substring(start,  i));
+                builder.append(".");
+
+            }else {
+                return;
+            }
+            backtracking(s,  i , num + 1);
+            builder.delete(builder.length() - (i-start)-1, builder.length());
+        }
+
+    }
+
+    @Test
+    public void test() {
+        String s = "03";
+//        System.out.println(s.substring(0, 0));
+        System.out.println(valid(s, 0, s.length()));
+    }
+
+    public boolean valid(String s, int start, int end) {
+        String substring = s.substring(start, end);
+        //避免开始是0的一个数
+        if(substring.length()>1&&substring.startsWith("0")){
+            return false;
+        }
+        int num = Integer.parseInt(substring);
+        return num >= 0 && num <= 255;
+    }
+
+
+    /**
+     * 官方回溯法
+     */
+    static final int SEG_COUNT = 4;
+    List<String> ans = new ArrayList<String>();
+    int[] segments = new int[SEG_COUNT];
+
+    public List<String> restoreIpAddresses1(String s) {
+        segments = new int[SEG_COUNT];
+        dfs(s, 0, 0);
+        return ans;
+    }
+
+    public void dfs(String s, int segId, int segStart) {
+        // 如果找到了 4 段 IP 地址并且遍历完了字符串，那么就是一种答案
+        if (segId == SEG_COUNT) {
+            if (segStart == s.length()) {
+                StringBuffer ipAddr = new StringBuffer();
+                for (int i = 0; i < SEG_COUNT; ++i) {
+                    ipAddr.append(segments[i]);
+                    if (i != SEG_COUNT - 1) {
+                        ipAddr.append('.');
+                    }
+                }
+                ans.add(ipAddr.toString());
+            }
+            return;
+        }
+
+        // 如果还没有找到 4 段 IP 地址就已经遍历完了字符串，那么提前回溯
+        if (segStart == s.length()) {
+            return;
+        }
+
+        // 由于不能有前导零，如果当前数字为 0，那么这一段 IP 地址只能为 0
+        if (s.charAt(segStart) == '0') {
+            segments[segId] = 0;
+            dfs(s, segId + 1, segStart + 1);
+        }
+
+        // 一般情况，枚举每一种可能性并递归
+        int addr = 0;
+        for (int segEnd = segStart; segEnd < s.length(); ++segEnd) {
+            addr = addr * 10 + (s.charAt(segEnd) - '0');
+            if (addr > 0 && addr <= 0xFF) {
+                segments[segId] = addr;
+                dfs(s, segId + 1, segEnd + 1);
+            } else {
+                break;
+            }
+        }
+    }
+
+}

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

@@ -6,5 +6,681 @@
 @Author : dingjiawen
 @Date : 2022/10/11 18:54
 @Usage : 
-@Desc :
+@Desc :  本人模型，只预测不分类，使用3西格玛原则
 '''
+
+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.Joint_Monitoring3 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_D"
+'''EWMA超参数'''
+K = 18
+namuda = 0.01
+'''保存名称'''
+save_name = "E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\hard_model\weight\joint_timestamp120_feature10_weight_epoch11_0.0077/weight"
+# save_name = "../hard_model/weight/{0}_timestamp{1}_feature{2}_weight_epoch11_0.0077/weight".format(model_name,
+#                                                                                            time_stamp,
+#                                                                                            feature_num,
+#                                                                                            batch_size,
+#                                                                                            EPOCH)
+save_step_two_name = "../hard_model/two_weight/{0}_timestamp{1}_feature{2}_weight_epoch14/weight".format(model_name,
+                                                                                                         time_stamp,
+                                                                                                         feature_num,
+                                                                                                         batch_size,
+                                                                                                         EPOCH)
+
+save_mse_name = "./mse/RNet_D/{0}_timestamp{1}_feature{2}_mse.csv".format(model_name,
+                                                                              time_stamp,
+                                                                              feature_num,
+                                                                              batch_size,
+                                                                              EPOCH)
+save_max_name = "./mse/RNet_D/{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
+
+
+
+
+
+def condition_monitoring_model():
+    input = tf.keras.Input(shape=[time_stamp, feature_num])
+    conv1 = tf.keras.layers.Conv1D(filters=256, kernel_size=1)(input)
+    GRU1 = tf.keras.layers.GRU(128, return_sequences=False)(conv1)
+    d1 = tf.keras.layers.Dense(300)(GRU1)
+    output = tf.keras.layers.Dense(10)(d1)
+
+    model = tf.keras.Model(inputs=input, outputs=output)
+
+    return model
+
+
+# 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[:32, :, :], train_label1=train_label1_healthy[:32, :],train_label2=train_label2_healthy[:32, ])
+    # 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)
+
+    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/condition_monitoring/self_try/compare/RNet-L.py

@@ -2,9 +2,655 @@
 
 # coding: utf-8
 
+
 '''
 @Author : dingjiawen
 @Date : 2022/10/11 18:53
-@Usage : 
+@Usage : 对比实验，与JointNet相同深度,LCAU,进行预测
 @Desc :
 '''
+
+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_L 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_L"
+'''EWMA超参数'''
+K = 18
+namuda = 0.01
+'''保存名称'''
+
+save_name = "./model/weight/{0}_timestamp{1}_feature{2}_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_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, 9))
+                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, 9))
+                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
+
+    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 in zip(mseList, meanList, maxList, mse1List):
+
+        # 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], ])
+
+        plt.figure(random.randint(1, 9))
+        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,:])
+
+    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)
+
+    # ###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/condition_monitoring/self_try/compare/RNet.py

@@ -477,37 +477,57 @@ def showResult(step_two_model: Joint_Monitoring, test_data, isPlot: bool = False
     return total_result
 
 
-def get_MSE(data, label, new_model, isStandard: bool = True, isPlot: bool = True):
+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 + 1, batch_size):
+    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)
-        predicted_data1.append(output1)
+        if epoch == 0:
-        predicted_data2.append(output2)
+            predicted_data1 = output1
-        predicted_data3.append(output3)
+            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])
-    temp = np.abs(predicted_data1 - label)
+    predict_data = 0
-    temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data1.shape))
+
-    temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data1.shape)
+    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("z:", mse)
-    print(mse.shape)
 
+        print("mse.shape:",mse.shape)
         # mse=np.mean((predicted_data-label)**2,axis=1)
-    print("mse", mse)
+        # 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:", max)
+            print("max.shape:", max.shape)
             # min = mean-3*std
             max = np.broadcast_to(max, shape=[dims, ])
             # min = np.broadcast_to(min,shape=[dims,])
@@ -519,24 +539,29 @@ def get_MSE(data, label, new_model, isStandard: bool = True, isPlot: bool = True
                 plt.plot(mean)
                 # plt.plot(min)
                 plt.show()
+            maxList.append(max)
+            meanList.append(mean)
         else:
             if isPlot:
                 plt.figure(random.randint(1, 9))
                 plt.plot(mse)
                 # plt.plot(min)
                 plt.show()
-            return mse
 
-    return mse, mean, max
+
+    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):
+              isSave: bool = False, predictI: int = 1):
     # TODO 计算MSE确定阈值
 
-    mse, mean, max = get_MSE(healthy_data, healthy_label, model)
+    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 in zip(mseList, meanList, maxList,mse1List):
 
         # 误报率的计算
         total, = mse.shape
@@ -553,7 +578,6 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
         # 漏报率计算
         missNum = 0
         missList = []
-    mse1 = get_MSE(unhealthy_data, unhealthy_label, model, isStandard=False)
         all, = mse1.shape
         for i in range(all):
             if (mse1[i] < max[0]):
@@ -562,6 +586,22 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
 
         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], ])
+
+        plt.figure(random.randint(1, 9))
+        plt.plot(total_max)
+        plt.plot(total_mse)
+        plt.plot(total_mean)
+        # plt.plot(min)
+        plt.show()
     pass
 
 
@@ -593,6 +633,14 @@ if __name__ == '__main__':
     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,:])
+
     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)

TensorFlow_eaxmple/Model_train_test/model/Dynamic_channelAttention/Light_channelAttention.py

@@ -0,0 +1,122 @@
+# _*_ coding: UTF-8 _*_
+
+
+'''
+@Author : dingjiawen
+@Date : 2022/7/12 17:48
+@Usage : 黄鸿海师兄的LCAU
+@Desc :
+'''
+
+import tensorflow as tf
+import tensorflow.keras
+from tensorflow.keras import *
+import tensorflow.keras.layers as layers
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from model.DepthwiseCon1D.DepthwiseConv1D import DepthwiseConv1D
+
+import keras.backend as K
+
+
+class Between_0_1(tf.keras.constraints.Constraint):
+    def __call__(self, w):
+        # 调用父类__init__()方法
+        super(Between_0_1, self).__init__()
+        return K.clip(w, 0, 1)
+
+
+class LightChannelAttention(layers.Layer):
+
+    def __init__(self):
+        # 调用父类__init__()方法
+        super(LightChannelAttention, self).__init__()
+        self.DWC = DepthwiseConv1D(kernel_size=1, padding='SAME')
+        # self.DWC = DepthwiseConv1D(kernel_size=1, padding='causal',dilation_rate=4,data_format='channels_last')
+
+    def build(self, input_shape):
+        if len(input_shape) != 3:
+            raise ValueError('Inputs to `DynamicChannelAttention` should have rank 3. '
+                             'Received input shape:', str(input_shape))
+
+        print(input_shape)
+        # GAP
+        self.GAP = tf.keras.layers.GlobalAvgPool1D()
+        self.c1 = tf.keras.layers.Conv1D(filters=input_shape[2], kernel_size=1, padding='SAME')
+        # s1 = tf.nn.sigmoid(c1)
+
+        # GMP
+        self.GMP = tf.keras.layers.GlobalMaxPool1D()
+        self.c2 = tf.keras.layers.Conv1D(filters=input_shape[2], kernel_size=1, padding='SAME')
+        # s2 = tf.nn.sigmoid(c2)
+
+
+
+    def call(self, inputs, **kwargs):
+        batch_size, length, channel = inputs.shape
+        DWC1 = self.DWC(inputs)
+
+        # GAP
+        GAP = self.GAP(DWC1)
+        GAP = tf.expand_dims(GAP, axis=1)
+        c1 = self.c1(GAP)
+        c1 = tf.keras.layers.BatchNormalization()(c1)
+        s1 = tf.nn.sigmoid(c1)
+
+        # # GMP
+        # GMP = self.GMP(DWC1)
+        # GMP = tf.expand_dims(GMP, axis=1)
+        # c2 = self.c2(GMP)
+        # c2 = tf.keras.layers.BatchNormalization()(c2)
+        # s2 = tf.nn.sigmoid(c2)
+
+        # print(self.weight_kernel)
+
+        # weight_kernel = tf.broadcast_to(self.weight_kernel, shape=[length, channel])
+        # weight_kernel = tf.broadcast_to(weight_kernel, shape=[batch_size, length, channel])
+        s1 = tf.broadcast_to(s1, shape=[batch_size, length, channel])
+        # s2 = tf.broadcast_to(s2, shape=[batch_size, length, channel])
+
+
+        return s1
+
+
+class DynamicPooling(layers.Layer):
+
+    def __init__(self, pool_size=2):
+        # 调用父类__init__()方法
+        super(DynamicPooling, self).__init__()
+        self.pool_size = pool_size
+        pass
+
+    def build(self, input_shape):
+        if len(input_shape) != 3:
+            raise ValueError('Inputs to `DynamicChannelAttention` should have rank 3. '
+                             'Received input shape:', str(input_shape))
+        # GAP
+        self.AP = tf.keras.layers.AveragePooling1D(pool_size=self.pool_size)
+
+        # GMP
+        self.MP = tf.keras.layers.MaxPool1D(pool_size=self.pool_size)
+
+        # weight
+        self.weight_kernel = self.add_weight(
+            shape=(int(input_shape[1] / self.pool_size), input_shape[2]),
+            initializer='glorot_uniform',
+            name='weight_kernel',
+            constraint=Between_0_1())
+
+    def call(self, inputs, **kwargs):
+        batch_size, length, channel = inputs.shape
+
+        # GAP
+        GAP = self.AP(inputs)
+
+        # GMP
+        GMP = self.MP(inputs)
+
+        weight_kernel = tf.broadcast_to(self.weight_kernel, shape=GMP.shape)
+
+        output = tf.add(weight_kernel * GAP, (tf.ones_like(weight_kernel) - weight_kernel) * GMP)
+        return output

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

@@ -0,0 +1,447 @@
+# _*_ coding: UTF-8 _*_
+
+
+'''
+@Author : dingjiawen
+@Date : 2022/7/14 9:40
+@Usage : 联合监测模型
+@Desc : RNet:去除掉DCAU
+'''
+
+import tensorflow as tf
+import tensorflow.keras as keras
+from tensorflow.keras import *
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from model.DepthwiseCon1D.DepthwiseConv1D import DepthwiseConv1D
+from model.Dynamic_channelAttention.Light_channelAttention import LightChannelAttention, DynamicPooling
+from condition_monitoring.data_deal import loadData
+from model.LossFunction.smooth_L1_Loss import SmoothL1Loss
+
+
+class Joint_Monitoring(keras.Model):
+
+    def __init__(self, conv_filter=20):
+        # 调用父类__init__()方法
+        super(Joint_Monitoring, self).__init__()
+        # step one
+        self.RepDCBlock1 = RevConvBlock(num=3, kernel_size=5)
+        self.conv1 = tf.keras.layers.Conv1D(filters=conv_filter, kernel_size=1, strides=2, padding='SAME')
+        self.upsample1 = tf.keras.layers.UpSampling1D(size=2)
+
+        self.DACU2 = LightChannelAttention()
+        self.RepDCBlock2 = RevConvBlock(num=3, kernel_size=3)
+        self.conv2 = tf.keras.layers.Conv1D(filters=2 * conv_filter, kernel_size=1, strides=2, padding='SAME')
+        self.upsample2 = tf.keras.layers.UpSampling1D(size=2)
+
+        self.DACU3 = LightChannelAttention()
+        self.RepDCBlock3 = RevConvBlock(num=3, kernel_size=3)
+        self.p1 = DynamicPooling(pool_size=2)
+        self.conv3 = tf.keras.layers.Conv1D(filters=2 * conv_filter, kernel_size=3, strides=2, padding='SAME')
+
+        self.DACU4 = LightChannelAttention()
+        self.RepDCBlock4 = RevConvBlock(num=3, kernel_size=3)
+        self.p2 = DynamicPooling(pool_size=4)
+        self.conv4 = tf.keras.layers.Conv1D(filters=conv_filter, kernel_size=3, strides=2, padding='SAME')
+
+        self.RepDCBlock5 = RevConvBlock(num=3, kernel_size=3)
+        self.p3 = DynamicPooling(pool_size=2)
+
+        # step two
+        # 重现原数据
+        self.GRU1 = tf.keras.layers.GRU(128, return_sequences=False)
+        self.d1 = tf.keras.layers.Dense(300, activation=tf.nn.leaky_relu)
+        self.output1 = tf.keras.layers.Dense(10, activation=tf.nn.leaky_relu)
+
+        self.GRU2 = tf.keras.layers.GRU(128, return_sequences=False)
+        self.d2 = tf.keras.layers.Dense(300, activation=tf.nn.leaky_relu)
+        self.output2 = tf.keras.layers.Dense(10, activation=tf.nn.leaky_relu)
+
+        self.GRU3 = tf.keras.layers.GRU(128, return_sequences=False)
+        self.d3 = tf.keras.layers.Dense(300, activation=tf.nn.leaky_relu)
+        self.output3 = tf.keras.layers.Dense(10, activation=tf.nn.leaky_relu)
+
+
+        # loss
+        self.train_loss = []
+
+    def call(self, inputs, training=None, mask=None, is_first_time: bool = True):
+        # step one
+        RepDCBlock1 = self.RepDCBlock1(inputs)
+        RepDCBlock1 = tf.keras.layers.BatchNormalization()(RepDCBlock1)
+        conv1 = self.conv1(RepDCBlock1)
+        conv1 = tf.nn.leaky_relu(conv1)
+        conv1 = tf.keras.layers.BatchNormalization()(conv1)
+        upsample1 = self.upsample1(conv1)
+
+        DACU2 = self.DACU2(upsample1)
+        DACU2 = tf.keras.layers.BatchNormalization()(DACU2)
+        RepDCBlock2 = self.RepDCBlock2(DACU2)
+        RepDCBlock2 = tf.keras.layers.BatchNormalization()(RepDCBlock2)
+        conv2 = self.conv2(RepDCBlock2)
+        conv2 = tf.nn.leaky_relu(conv2)
+        conv2 = tf.keras.layers.BatchNormalization()(conv2)
+        upsample2 = self.upsample2(conv2)
+
+        DACU3 = self.DACU3(upsample2)
+        DACU3 = tf.keras.layers.BatchNormalization()(DACU3)
+        RepDCBlock3 = self.RepDCBlock3(DACU3)
+        RepDCBlock3 = tf.keras.layers.BatchNormalization()(RepDCBlock3)
+        conv3 = self.conv3(RepDCBlock3)
+        conv3 = tf.nn.leaky_relu(conv3)
+        conv3 = tf.keras.layers.BatchNormalization()(conv3)
+
+        concat1 = tf.concat([conv2, conv3], axis=1)
+
+        DACU4 = self.DACU4(concat1)
+        DACU4 = tf.keras.layers.BatchNormalization()(DACU4)
+        RepDCBlock4 = self.RepDCBlock4(DACU4)
+        RepDCBlock4 = tf.keras.layers.BatchNormalization()(RepDCBlock4)
+        conv4 = self.conv4(RepDCBlock4)
+        conv4 = tf.nn.leaky_relu(conv4)
+        conv4 = tf.keras.layers.BatchNormalization()(conv4)
+
+        concat2 = tf.concat([conv1, conv4], axis=1)
+
+        RepDCBlock5 = self.RepDCBlock5(concat2)
+        RepDCBlock5 = tf.keras.layers.BatchNormalization()(RepDCBlock5)
+
+        output1 = []
+        output2 = []
+        output3 = []
+        output4 = []
+
+        if is_first_time:
+            # step two
+            # 重现原数据
+            # 接block3
+            GRU1 = self.GRU1(RepDCBlock3)
+            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)
+            d2 = self.d2(GRU2)
+            # tf.nn.softmax
+            output2 = self.output2(d2)
+            # 接block5
+            GRU3 = self.GRU3(RepDCBlock5)
+            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)
+            d1 = self.d1(GRU1)
+            # tf.nn.softmax
+            output1 = self.output1(d1)
+            # 接block4
+            GRU2 = self.GRU2(RepDCBlock4)
+            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)
+            d3 = self.d3(GRU3)
+            # tf.nn.softmax
+            output3 = self.output3(d3)
+
+            # 多尺度动态池化
+            # p1 = self.p1(output1)
+            # B, _, _ = p1.shape
+            # f1 = tf.reshape(p1, shape=[B, -1])
+            # p2 = self.p2(output2)
+            # f2 = tf.reshape(p2, shape=[B, -1])
+            # p3 = self.p3(output3)
+            # f3 = tf.reshape(p3, shape=[B, -1])
+            # step three
+            # 分类器
+            concat3 = tf.concat([output1, output2, output3], axis=1)
+            # dropout = tf.keras.layers.Dropout(0.25)(concat3)
+            d4 = self.d4(concat3)
+            d5 = self.d5(d4)
+            # d4 = tf.keras.layers.BatchNormalization()(d4)
+            output4 = self.output4(d5)
+
+        return output1, output2, output3, output4
+
+    def get_loss(self, inputs_tensor, label1=None, label2=None, is_first_time: bool = True, pred_3=None, pred_4=None,
+                 pred_5=None):
+        # step one
+        RepDCBlock1 = self.RepDCBlock1(inputs_tensor)
+        RepDCBlock1 = tf.keras.layers.BatchNormalization()(RepDCBlock1)
+        conv1 = self.conv1(RepDCBlock1)
+        conv1 = tf.nn.leaky_relu(conv1)
+        conv1 = tf.keras.layers.BatchNormalization()(conv1)
+        upsample1 = self.upsample1(conv1)
+
+        DACU2 = self.DACU2(upsample1)
+        DACU2 = tf.keras.layers.BatchNormalization()(DACU2)
+        RepDCBlock2 = self.RepDCBlock2(DACU2)
+        RepDCBlock2 = tf.keras.layers.BatchNormalization()(RepDCBlock2)
+        conv2 = self.conv2(RepDCBlock2)
+        conv2 = tf.nn.leaky_relu(conv2)
+        conv2 = tf.keras.layers.BatchNormalization()(conv2)
+        upsample2 = self.upsample2(conv2)
+
+        DACU3 = self.DACU3(upsample2)
+        DACU3 = tf.keras.layers.BatchNormalization()(DACU3)
+        RepDCBlock3 = self.RepDCBlock3(DACU3)
+        RepDCBlock3 = tf.keras.layers.BatchNormalization()(RepDCBlock3)
+        conv3 = self.conv3(RepDCBlock3)
+        conv3 = tf.nn.leaky_relu(conv3)
+        conv3 = tf.keras.layers.BatchNormalization()(conv3)
+
+        concat1 = tf.concat([conv2, conv3], axis=1)
+
+        DACU4 = self.DACU4(concat1)
+        DACU4 = tf.keras.layers.BatchNormalization()(DACU4)
+        RepDCBlock4 = self.RepDCBlock4(DACU4)
+        RepDCBlock4 = tf.keras.layers.BatchNormalization()(RepDCBlock4)
+        conv4 = self.conv4(RepDCBlock4)
+        conv4 = tf.nn.leaky_relu(conv4)
+        conv4 = tf.keras.layers.BatchNormalization()(conv4)
+
+        concat2 = tf.concat([conv1, conv4], axis=1)
+
+        RepDCBlock5 = self.RepDCBlock5(concat2)
+        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)
+            d1 = self.d1(GRU1)
+            # tf.nn.softmax
+            output1 = self.output1(d1)
+            # 接block4
+            GRU2 = self.GRU2(RepDCBlock4)
+            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)
+            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_loss2 = SmoothL1Loss()(y_true=label1, y_pred=output2)
+            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_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))
+
+            print("MSE_loss1:", MSE_loss1.numpy())
+            print("MSE_loss2:", MSE_loss2.numpy())
+            print("MSE_loss3:", MSE_loss3.numpy())
+            loss = MSE_loss1 + MSE_loss2 + MSE_loss3
+            Accuracy_num = 0
+
+        else:
+            # step two
+            # 重现原数据
+            # 接block3
+            GRU1 = self.GRU1(RepDCBlock3)
+            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)
+            d2 = self.d2(GRU2)
+            # tf.nn.softmax
+            output2 = self.output2(d2)
+            # 接block5
+            GRU3 = self.GRU3(RepDCBlock5)
+            GRU3 = tf.keras.layers.BatchNormalization()(GRU3)
+            d3 = self.d3(GRU3)
+            # tf.nn.softmax
+            output3 = self.output3(d3)
+
+            # 多尺度动态池化
+            # p1 = self.p1(output1)
+            # B, _, _ = p1.shape
+            # f1 = tf.reshape(p1, shape=[B, -1])
+            # p2 = self.p2(output2)
+            # f2 = tf.reshape(p2, shape=[B, -1])
+            # p3 = self.p3(output3)
+            # f3 = tf.reshape(p3, shape=[B, -1])
+            # step three
+            # 分类器
+            concat3 = tf.concat([output1, output2, output3], axis=1)
+            # dropout = tf.keras.layers.Dropout(0.25)(concat3)
+            d4 = self.d4(concat3)
+            d5 = self.d5(d4)
+            # d4 = tf.keras.layers.BatchNormalization()(d4)
+            output4 = self.output4(d5)
+
+            # reduce_mean降维计算均值
+            MSE_loss = SmoothL1Loss()(y_true=pred_3, y_pred=output1)
+            MSE_loss += SmoothL1Loss()(y_true=pred_4, y_pred=output2)
+            MSE_loss += SmoothL1Loss()(y_true=pred_5, y_pred=output3)
+            Cross_Entropy_loss = tf.reduce_mean(
+                tf.losses.binary_crossentropy(y_true=label2, y_pred=output4, from_logits=True))
+
+            print("MSE_loss:", MSE_loss.numpy())
+            print("Cross_Entropy_loss:", Cross_Entropy_loss.numpy())
+            Accuracy_num = self.get_Accuracy(label=label2, output=output4)
+            loss = MSE_loss + Cross_Entropy_loss
+        return loss, Accuracy_num
+
+    def get_Accuracy(self, output, label):
+
+        predict_label = tf.round(output)
+        label = tf.cast(label, dtype=tf.float32)
+
+        t = np.array(label - predict_label)
+
+        b = t[t[:] == 0]
+
+        return b.__len__()
+
+    def get_grad(self, input_tensor, label1=None, label2=None, is_first_time: bool = True, pred_3=None, pred_4=None,
+                 pred_5=None):
+        with tf.GradientTape() as tape:
+            # todo 原本tape只会监控由tf.Variable创建的trainable=True属性
+            # tape.watch(self.variables)
+            L, Accuracy_num = self.get_loss(input_tensor, label1=label1, label2=label2, is_first_time=is_first_time,
+                                            pred_3=pred_3,
+                                            pred_4=pred_4, pred_5=pred_5)
+            # 保存一下loss，用于输出
+            self.train_loss = L
+            g = tape.gradient(L, self.variables)
+        return g, Accuracy_num
+
+    def train(self, input_tensor, label1=None, label2=None, learning_rate=1e-3, is_first_time: bool = True, pred_3=None,
+              pred_4=None, pred_5=None):
+        g, Accuracy_num = self.get_grad(input_tensor, label1=label1, label2=label2, is_first_time=is_first_time,
+                                        pred_3=pred_3,
+                                        pred_4=pred_4, pred_5=pred_5)
+        optimizers.Adam(learning_rate).apply_gradients(zip(g, self.variables))
+        return self.train_loss, Accuracy_num
+
+    # 暂时只支持batch_size等于1,不然要传z比较麻烦
+    def get_val_loss(self, val_data, val_label1, val_label2, batch_size=16, is_first_time: bool = True,
+                     step_one_model=None):
+        val_loss = []
+        accuracy_num = 0
+        output1 = 0
+        output2 = 0
+        output3 = 0
+        z = 1
+        size, length, dims = val_data.shape
+        if batch_size == None:
+            batch_size = self.batch_size
+        for epoch in range(0, size - batch_size, batch_size):
+            each_val_data = val_data[epoch:epoch + batch_size, :, :]
+            each_val_label1 = val_label1[epoch:epoch + batch_size, :]
+            each_val_label2 = val_label2[epoch:epoch + batch_size, ]
+            # each_val_data = tf.expand_dims(each_val_data, axis=0)
+            # each_val_query = tf.expand_dims(each_val_query, axis=0)
+            # each_val_label = tf.expand_dims(each_val_label, axis=0)
+            if not is_first_time:
+                output1, output2, output3, _ = step_one_model.call(inputs=each_val_data, is_first_time=True)
+
+            each_loss, each_accuracy_num = self.get_loss(each_val_data, each_val_label1, each_val_label2,
+                                                         is_first_time=is_first_time,
+                                                         pred_3=output1, pred_4=output2, pred_5=output3)
+            accuracy_num += each_accuracy_num
+            val_loss.append(each_loss)
+            z += 1
+
+        val_accuracy = accuracy_num / ((z-1) * batch_size)
+        val_total_loss = tf.reduce_mean(val_loss)
+        return val_total_loss, val_accuracy
+
+
+class RevConv(keras.layers.Layer):
+
+    def __init__(self, kernel_size=3):
+        # 调用父类__init__()方法
+        super(RevConv, self).__init__()
+        self.kernel_size = kernel_size
+
+    def get_config(self):
+        # 自定义层里面的属性
+        config = (
+            {
+                'kernel_size': self.kernel_size
+            }
+        )
+        base_config = super(RevConv, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+    def build(self, input_shape):
+        # print(input_shape)
+        _, _, output_dim = input_shape[0], input_shape[1], input_shape[2]
+        self.conv1 = tf.keras.layers.Conv1D(filters=output_dim, kernel_size=self.kernel_size, strides=1,
+                                            padding='causal',
+                                            dilation_rate=4)
+
+        self.conv2 = tf.keras.layers.Conv1D(filters=output_dim, kernel_size=1, strides=1, padding='causal',
+                                            dilation_rate=4)
+        # self.b2 = tf.keras.layers.BatchNormalization()
+
+        # self.b3 = tf.keras.layers.BatchNormalization()
+
+        # out = tf.keras.layers.Add()([b1, b2, b3])
+        # out = tf.nn.relu(out)
+
+    def call(self, inputs, **kwargs):
+        conv1 = self.conv1(inputs)
+        b1 = tf.keras.layers.BatchNormalization()(conv1)
+        b1 = tf.nn.leaky_relu(b1)
+        # b1 = self.b1
+
+        conv2 = self.conv2(inputs)
+        b2 = tf.keras.layers.BatchNormalization()(conv2)
+        b2 = tf.nn.leaky_relu(b2)
+
+        b3 = tf.keras.layers.BatchNormalization()(inputs)
+
+        out = tf.keras.layers.Add()([b1, b2, b3])
+        out = tf.nn.relu(out)
+
+        return out
+
+
+class RevConvBlock(keras.layers.Layer):
+
+    def __init__(self, num: int = 3, kernel_size=3):
+        # 调用父类__init__()方法
+        super(RevConvBlock, self).__init__()
+        self.num = num
+        self.kernel_size = kernel_size
+        self.L = []
+        for i in range(num):
+            RepVGG = RevConv(kernel_size=kernel_size)
+            self.L.append(RepVGG)
+
+    def get_config(self):
+        # 自定义层里面的属性
+        config = (
+            {
+                'kernel_size': self.kernel_size,
+                'num': self.num
+            }
+        )
+        base_config = super(RevConvBlock, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+    def call(self, inputs, **kwargs):
+        for i in range(self.num):
+            inputs = self.L[i](inputs)
+        return inputs