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@@ -6,11 +6,10 @@
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# company: shenyang JY
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import numpy as np
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-from sklearn.model_selection import train_test_split
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from flask import Flask, request
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import time
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import traceback
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-import logging
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+import logging, argparse
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from sklearn.preprocessing import MinMaxScaler
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from io import BytesIO
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import joblib
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@@ -20,39 +19,27 @@ from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, TensorBoa
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from tensorflow.keras import optimizers, regularizers
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import tensorflow.keras.backend as K
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import tensorflow as tf
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-
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from common.data_cleaning import cleaning
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from common.database_dml import *
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from common.processing_data_common import missing_features, str_to_list
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from data_processing.data_operation.data_handler import DataHandler
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from threading import Lock
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-import time
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+import time, yaml
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import random
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import matplotlib.pyplot as plt
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model_lock = Lock()
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+from common.logs import Log
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+logger = Log('models-processing').logger
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+np.random.seed(42) # NumPy随机种子
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+tf.random.set_random_seed(42) # TensorFlow随机种子
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+app = Flask('nn_bp——service')
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+with app.app_context():
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+ with open('./models_processing/model_koi/bp.yaml', 'r', encoding='utf-8') as f:
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+ arguments = yaml.safe_load(f)
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-app = Flask('model_training_bp——service')
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-
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-def draw_loss(history):
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- # 绘制训练集和验证集损失
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- plt.figure(figsize=(20, 8))
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- plt.plot(history.history['loss'], label='Training Loss')
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- plt.plot(history.history['val_loss'], label='Validation Loss')
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- plt.title('Loss Curve')
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- plt.xlabel('Epochs')
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- plt.ylabel('Loss')
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- plt.legend()
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- plt.show()
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-
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-dh = DataHandler()
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+dh = DataHandler(logger, arguments)
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def train_data_handler(data, args):
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- sleep_time = random.uniform(1, 20) # 生成 5 到 20 之间的随机浮动秒数
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- time.sleep(sleep_time)
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- tf.keras.backend.clear_session() # 清除当前的图和会话
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- # 设置随机种子
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- np.random.seed(42) # NumPy随机种子
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- tf.random.set_seed(42) # TensorFlow随机种子
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col_time, features, target = args['col_time'], str_to_list(args['features']), args['target']
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if 'is_limit' in data.columns:
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data = data[data['is_limit'] == False]
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@@ -78,20 +65,12 @@ def pre_data_handler(data, args):
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features, time_steps, col_time, model_name,col_reserve = str_to_list(args['features']), int(args['time_steps']),args['col_time'],args['model_name'],str_to_list(args['col_reserve'])
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feature_scaler,target_scaler = get_scaler_model_from_mongo(args)
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pre_data = data.sort_values(by=col_time)
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- # 对预测数据进行特征预处理：1.空值异常值清洗 2.缺值补值
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- pre_data_cleaned = cleaning(pre_data, '', logger, pre_data.columns.tolist())
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- pre_data = dh.fill_train_data(pre_data_cleaned)
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scaled_features = feature_scaler.transform(pre_data[features])
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return scaled_features
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-class NPHandler(object):
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- train = False
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-
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- def __init__(self, log, args, graph, sess):
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- self.logger = log
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- self.graph = graph
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- self.sess = sess
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- opt = args.parse_args_and_yaml()
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+class BPHandler(object):
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+ def __init__(self, logger):
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+ self.logger = logger
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self.model = None
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def get_model(self, args):
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@@ -103,7 +82,7 @@ class NPHandler(object):
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# NPHandler.model = NPHandler.get_keras_model(opt)
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self.model = get_h5_model_from_mongo(args)
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except Exception as e:
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- print("加载模型权重失败:{}".format(e.args))
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+ self.logger.info("加载模型权重失败:{}".format(e.args))
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@staticmethod
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def get_keras_model(opt):
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@@ -124,14 +103,14 @@ class NPHandler(object):
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adam = optimizers.Adam(learning_rate=opt.Model['learning_rate'], beta_1=0.9, beta_2=0.999, epsilon=1e-7,
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amsgrad=True)
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reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.01, patience=5, verbose=1)
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- model.compile(loss=rmse, optimizer=adam)
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+ model.compile(loss='rmse', optimizer=adam)
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return model
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- def train_init(self, opt, args):
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+ def train_init(self, opt):
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try:
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if opt.Model['add_train']:
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# 进行加强训练，支持修模
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- base_train_model = get_h5_model_from_mongo(args)
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+ base_train_model = get_h5_model_from_mongo(vars(opt))
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base_train_model.summary()
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self.logger.info("已加载加强训练基础模型")
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else:
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@@ -141,23 +120,17 @@ class NPHandler(object):
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self.logger.info("加强训练加载模型权重失败:{}".format(e.args))
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def training(self, opt, train_and_valid_data):
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- model = self.train_init(opt)
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- train_X, train_Y, valid_X, valid_Y = train_and_valid_data
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- print("----------", np.array(train_X[0]).shape)
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- print("++++++++++", np.array(train_X[1]).shape)
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- # weight_lstm_1, bias_lstm_1 = model.get_layer('d1').get_weights()
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- # print("weight_lstm_1 = ", weight_lstm_1)
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- # print("bias_lstm_1 = ", bias_lstm_1)
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+ model = self.train_init(opt, )
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+ tf.reset_default_graph() # 清除默认图
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+ train_x, train_y, valid_x, valid_y = train_and_valid_data
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+ print("----------", np.array(train_x[0]).shape)
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+ print("++++++++++", np.array(train_x[1]).shape)
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check_point = ModelCheckpoint(filepath='./var/' + 'fmi.h5', monitor='val_loss',
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save_best_only=True, mode='auto')
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early_stop = EarlyStopping(monitor='val_loss', patience=opt.Model['patience'], mode='auto')
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- # tbCallBack = TensorBoard(log_dir='../figure',
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- # histogram_freq=0,
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- # write_graph=True,
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- # write_images=True)
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- history = model.fit(train_X, train_Y, batch_size=opt.Model['batch_size'], epochs=opt.Model['epoch'], verbose=2,
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- validation_data=(valid_X, valid_Y), callbacks=[check_point, early_stop], shuffle=False)
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+ history = model.fit(train_x, train_y, batch_size=opt.Model['batch_size'], epochs=opt.Model['epoch'], verbose=2,
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+ validation_data=(valid_x, valid_y), callbacks=[check_point, early_stop], shuffle=False)
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loss = np.round(history.history['loss'], decimals=5)
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val_loss = np.round(history.history['val_loss'], decimals=5)
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self.logger.info("-----模型训练经过{}轮迭代-----".format(len(loss)))
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@@ -170,56 +143,25 @@ class NPHandler(object):
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self.logger.info("执行预测方法")
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return result
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-def build_model(data, args):
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- # 划分训练集和测试集
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- X_train, X_test, y_train, y_test = train_test_split(scaled_features, scaled_target, test_size=0.2, random_state=43)
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-
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- # 构建 LSTM 模型
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- model = Sequential([
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- Dense(64, input_dim=X_train.shape[1], activation='relu'), # 输入层和隐藏层，10个神经元
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- Dropout(0.2),
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- Dense(32, activation='relu'), # 隐藏层，8个神经元
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- Dropout(0.3), # Dropout层，30%的神经元输出会被随机丢弃
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- Dense(1, activation='linear') # 输出层，1个神经元（用于回归任务）
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- ])
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-
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- # 编译模型
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- model.compile(optimizer='adam', loss='mean_squared_error')
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- # 定义 EarlyStopping 和 ReduceLROnPlateau 回调
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- early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True, verbose=1)
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- reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, verbose=1)
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- # 训练模型
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- # 使用GPU进行训练
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- with tf.device('/GPU:1'):
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- history = model.fit(X_train, y_train,
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- epochs=100,
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- batch_size=32,
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- validation_data=(X_test, y_test),
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- verbose=2,
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- shuffle=False,
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- callbacks=[early_stopping, reduce_lr])
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- draw_loss(history)
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- return model, feature_scaler_bytes, target_scaler_bytes
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-
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-
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@app.route('/model_training_bp', methods=['POST'])
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def model_training_bp():
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# 获取程序开始时间
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start_time = time.time()
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result = {}
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success = 0
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- nh = NPHandler()
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+ bp = BPHandler(logger)
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print("Program starts execution!")
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try:
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- args = request.values.to_dict()
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- print('args', args)
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+ params_dict = request.values.to_dict()
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+ args = arguments.deepcopy()
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+ args.update(params_dict)
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+ opt = argparse.Namespace(**args)
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logger.info(args)
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- power_df = get_data_from_mongo(args)
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- train_x, valid_x, train_y, valid_y, train_data_handler = dh.get_train_data(power_df)
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- np_model = nh.training(opt, [train_x, valid_x, train_y, valid_y])
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- model, feature_scaler_bytes, target_scaler_bytes = build_model(power_df, args)
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-
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- insert_h5_model_into_mongo(np_model, train_data_handler, args)
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+ train_data = get_data_from_mongo(args)
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+ train_x, valid_x, train_y, valid_y, scaled_train_bytes = train_data_handler(train_data, args)
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+ bp_model = bp.training(opt, [train_x, valid_x, train_y, valid_y])
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+ insert_single_model_into_mongo(bp_model, args)
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+ insert_scaler_model_into_mongo(scaled_train_bytes, args)
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success = 1
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except Exception as e:
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my_exception = traceback.format_exc()
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@@ -241,15 +183,18 @@ def model_prediction_bp():
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start_time = time.time()
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result = {}
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success = 0
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- nh = NPHandler()
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+ bp = BPHandler(logger)
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print("Program starts execution!")
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try:
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- args = request.values.to_dict()
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+ params_dict = request.values.to_dict()
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+ args = arguments.deepcopy()
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+ args.update(params_dict)
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+ opt = argparse.Namespace(**args)
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print('args', args)
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logger.info(args)
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- power_df = get_data_from_mongo(args)
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- scaled_features = pre_data_handler(power_df, args)
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- result = nh.predict(power_df, args)
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+ predict_data = get_data_from_mongo(args)
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+ scaled_features = pre_data_handler(predict_data, args)
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+ result = bp.predict(scaled_features, args)
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insert_data_into_mongo(result, args)
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success = 1
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except Exception as e:
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