03041303

data_processing/data_operation/pre_prod_ftp.py

@@ -0,0 +1,10 @@
+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+# @FileName  :pre_prod_ftp.py
+# @Time      :2025/3/4 13:02
+# @Author    :David
+# @Company: shenyang JY
+ 
+ 
+if __name__ == "__main__":
+    run_code = 0

models_processing/model_koi/tf_lstm.py

@@ -43,7 +43,7 @@ class TSHandler(object):
 
         con1 = Conv1D(filters=64, kernel_size=5, strides=1, padding='valid', activation='relu', kernel_regularizer=l2_reg)(nwp_input)
         con1_p = MaxPooling1D(pool_size=5, strides=1, padding='valid', data_format='channels_last')(con1)
-        nwp_lstm = LSTM(units=opt.Model['hidden_size'], return_sequences=False, kernel_regularizer=l2_reg)(nwp_input)
+        nwp_lstm = LSTM(units=opt.Model['hidden_size'], return_sequences=False, kernel_regularizer=l2_reg)(con1_p)
 
         output = Dense(opt.Model['output_size'], name='cdq_output')(nwp_lstm)
 

models_processing/model_koi/tf_lstm_train.py

@@ -87,21 +87,21 @@ if __name__ == "__main__":
 
     serve(app, host="0.0.0.0", port=10103, threads=4)
     print("server start!")
-    # args_dict = {"mongodb_database": 'david_test', 'scaler_table': 'j00083_scaler', 'model_name': 'bp1.0.test',
-    # 'model_table': 'j00083_model', 'mongodb_read_table': 'j00083', 'col_time': 'dateTime',
+    # args_dict = {"mongodb_database": 'realtimeDq', 'scaler_table': 'j00600_scaler', 'model_name': 'lstm1',
+    # 'model_table': 'j00600_model', 'mongodb_read_table': 'j00600', 'col_time': 'dateTime',
     # 'features': 'speed10,direction10,speed30,direction30,speed50,direction50,speed70,direction70,speed90,direction90,speed110,direction110,speed150,direction150,speed170,direction170'}
     # args_dict['features'] = args_dict['features'].split(',')
-    # arguments.update(args_dict)
-    # dh = DataHandler(logger, arguments)
-    # ts = TSHandler(logger)
-    # opt = argparse.Namespace(**arguments)
+    # args.update(args_dict)
+    # dh = DataHandler(logger, args)
+    # ts = TSHandler(logger, args)
+    # opt = argparse.Namespace(**args)
     # opt.Model['input_size'] = len(opt.features)
     # train_data = get_data_from_mongo(args_dict)
-    # train_x, valid_x, train_y, valid_y, scaled_train_bytes, scaled_target_bytes = dh.train_data_handler(train_data, opt)
-    # ts_model = ts.training(opt, [train_x, train_y, valid_x, valid_y])
+    # train_x, train_y, valid_x, valid_y, scaled_train_bytes, scaled_target_bytes = dh.train_data_handler(train_data)
+    # ts_model = ts.training([train_x, train_y, valid_x, valid_y])
     #
     # args_dict['gen_time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
-    # args_dict['params'] = arguments
+    # args_dict['params'] = args
     # args_dict['descr'] = '测试'
     # insert_trained_model_into_mongo(ts_model, args_dict)
     # insert_scaler_model_into_mongo(scaled_train_bytes, scaled_target_bytes, args_dict)