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markilue 2022-10-17 13:26:09 +08:00
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168
Leecode/src/main/java/com/markilue/leecode/backtrace/RestoreIpAddresses.java Normal file
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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;
}
}
}
}

680
TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-D.py
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@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

650
TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-L.py
View File

@ -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

172
TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet.py
View File

@ -477,91 +477,131 @@ 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)
predicted_data2.append(output2)
predicted_data3.append(output3)
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])
temp = np.abs(predicted_data1 - label)
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)
temp3 = temp1 / temp2
mse = np.sum((temp1 / temp2) ** 2, axis=1)
print("z:", mse)
print(mse.shape)
predict_data = 0
# mse=np.mean((predicted_data-label)**2,axis=1)
print("mse", mse)
if isStandard:
dims, = mse.shape
mean = np.mean(mse)
std = np.sqrt(np.var(mse))
max = mean + 3 * std
print("max:", max)
# 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()
else:
if isPlot:
plt.figure(random.randint(1, 9))
plt.plot(mse)
# plt.plot(min)
plt.show()
return mse
predict_data = predicted_data1
mseList=[]
meanList=[]
maxList=[]
return mse, mean, max
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):
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)
# 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])
for mse,mean,max,mse1 in zip(mseList, meanList, maxList,mse1List):
fault_rate = faultNum / total
print("误报率:", fault_rate)
# 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])
# 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]):
missNum += 1
missList.append(mse1[i])
fault_rate = faultNum / total
print("误报率:", fault_rate)
miss_rate = missNum / all
print("漏报率:", miss_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
@ -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)

122
TensorFlow_eaxmple/Model_train_test/model/Dynamic_channelAttention/Light_channelAttention.py Normal file
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@ -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

447
TensorFlow_eaxmple/Model_train_test/model/Joint_Monitoring/compare/RNet_L.py Normal file
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@ -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