AANN_Fit.py

@@ -4,9 +4,6 @@ import config
 import json
 import sys
 import requests
-import datetime
-import jenkspy
-import xlrd
 import AANN_Fit
 import traceback
 

ANN_Train_offline.py

@@ -17,10 +17,9 @@ import pandas as pd
 import requests
 import tensorflow as tf
 from sklearn.preprocessing import MinMaxScaler
-from tensorflow.keras import backend
-from tensorflow.keras import layers
-from tensorflow.keras.models import load_model
-from tensorflow.keras.models import model_from_json
+import keras
+from keras import backend
+from keras import layers
 
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
@@ -53,7 +52,7 @@ def get_history_value(points, time, interval, typedata):
 
 
 def rmse(y_true, y_pred):
-    return backend.sqrt(backend.mean(tf.keras.losses.mean_squared_error(y_true, y_pred), axis=-1))
+    return backend.sqrt(backend.mean(keras.losses.mean_squared_error(y_true, y_pred), axis=-1))
 
 
 def TrainOffline(x_data, y_data, hidden_layers, epochsdata):
@@ -73,7 +72,7 @@ def TrainOffline(x_data, y_data, hidden_layers, epochsdata):
     x_train = x_normal
     y_train = y_normal
     # 构建网络结构
-    model = tf.keras.Sequential()
+    model = keras.Sequential()
     model.add(layers.Dense(units=hidden_layers[0], input_dim=x_data.shape[1], activation="sigmoid"))
     for i in range(len(hidden_layers) - 1):
         model.add(layers.Dense(units=hidden_layers[i + 1], activation="sigmoid"))
@@ -150,7 +149,7 @@ def Train(x_data, y_data, hidden_layers, valuetrs, epochsdata):
     x_train = x_normal
     y_train = y_normal
     # 构建网络结构
-    model = tf.keras.Sequential()
+    model = keras.Sequential()
     model.add(layers.Dense(units=hidden_layers[0], input_dim=x_data.shape[1], activation="sigmoid"))
     for i in range(len(hidden_layers) - 1):
         model.add(layers.Dense(units=hidden_layers[i + 1], activation="sigmoid"))

PCA_Test.py

@@ -5,24 +5,10 @@ PCA source code
 @author: liudiwei
 """
 
-import xlsxwriter as xw
 import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from scipy.stats import norm
-from scipy.stats.distributions import chi2
 import json
-import sys
 import pymssql
 import requests
-import datetime
-from scipy.stats import norm
-from scipy.stats import f
-from scipy.stats import chi2
-import jenkspy
-import xlrd
-import time
-# import PCA_Test_offline
 import config
 
 

PCA_Test_offline.py

@@ -7,21 +7,11 @@ PCA source code————最新更新—————————————
 
 
 import numpy as np
-import pandas as pd
-from scipy.stats import norm
-from scipy.stats.distributions import chi2
 import json
-import sys
 import pymssql
 import requests
-import datetime
-from scipy.stats import norm
-from scipy.stats import f
-from scipy.stats import chi2
 import jenkspy
-import xlrd
 import gc
-import time
 import pyodbc
 from recon import Lars, recon_fault_diagnosis_r, recon_fault_diagnosis_r_l, recon_fault_diagnosis_r_c
 import config

app.py

@@ -1,20 +1,12 @@
 # -*- coding: utf-8 -*-
-import datetime
 import json
-import json
-import sys
 import traceback
 
 import PCA_Test
 import PCA_Test_offline
-import config
-import jenkspy
 import numpy as np
-import requests
-import xlrd
 from flask import Flask
 from flask import request
-from numba import jit
 
 app = Flask(__name__)