基于图像识别的检测城市的整体异常.zip

''' author: cbz ''' import keras from keras.models import Sequential from keras.models import load_model from keras.layers import Dense from keras.callbacks import ReduceLROnPlateau from process import makedata from util import * import pandas as pd import numpy as np from queue import Queue import time import json ''' train method 1:311 2:bikeinflow 3:bikeoutflow 4:taxiinflow 5:taxiouflow ''' def train1(region): path = 'model/' + str(region) + '311.h5' file = open(path,'w') rawdata = pd.read_csv('model/' + str(region) + '311.csv').values if len(rawdata) == 0: print('Region' + str(region) + ' don\'t have data about 311!\nUse the unified model!') return dic = makedata(region,'model/' + str(region) + '311.csv') x = np.array(dic['x']) y = np.array(dic['y']) model = None if os.path.getsize('model/' + str(region) + '311.h5') == 0: model = Sequential() model.add(Dense(10,activation='sigmoid',use_bias=True)) model.add(Dense(1,use_bias=True)) model.compile(loss=keras.losses.mean_squared_error, optimizer=keras.optimizers.RMSprop(0.1)) else: model = load_model('model/' + str(region) + '311.h5') reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto') model.fit(x=x, y=y, epochs=100, validation_split=0.1, verbose = 0, shuffle=True, batch_size=128, callbacks=[reduce_lr]) model.save('model/' + str(region) + '311.h5') print(str(region) + '-311:100 epoches have been trained over!') return def train2(region): # path = 'model/' + str(region) + '/bikeinflow.h5' # file = open(path, 'w') rawdata = pd.read_csv('model/' + str(region) + '/bikeinflow.csv').values if len(rawdata) == 0: print('Region' + str(region) + ' don\'t have data about bikeinflow!\nUse the unified model!') # return dic = makedata(region, 'model/' + str(region) + '/bikeinflow.csv') x = np.array(dic['x']) y = np.array(dic['y']) model = None if os.path.getsize('model/' + str(region) + '/bikeinflow.h5') == 0: print('new model') model = Sequential() model.add(Dense(10, input_shape=(1,), activation='sigmoid', use_bias=True)) model.add(Dense(1, use_bias=True)) model.compile(loss=keras.losses.mean_squared_error, optimizer=keras.optimizers.RMSprop(0.1)) else: model = load_model('model/' + str(region) + '/bikeinflow.h5') reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto') model.fit(x=x, y=y, epochs=100, validation_split=0.1, verbose = 0, shuffle=True, batch_size=128, callbacks=[reduce_lr]) model.save('model/' + str(region) + '/bikeinflow.h5') print(str(region) + '-bikeinflow:100 epoches have been trained over!') return def train3(region): # path = 'model/' + str(region) + '/bikeoutflow.h5' # file = open(path, 'w') rawdata = pd.read_csv('model/' + str(region) + '/bikeoutflow.csv').values if len(rawdata) == 0: print('Region' + str(region) + ' don\'t have data about bikeoutflow!\nUse the unified model!') # return dic = makedata(region, 'model/' + str(region) + '/bikeoutflow.csv') x = np.array(dic['x']) y = np.array(dic['y']) model = None if os.path.getsize('model/' + str(region) + '/bikeoutflow.h5') == 0: model = Sequential() model.add(Dense(10, input_shape=(1,),activation='sigmoid', use_bias=True)) model.add(Dense(1, use_bias=True)) model.compile(loss=keras.losses.mean_squared_error, optimizer=keras.optimizers.RMSprop(0.1)) else: model = load_model('model/' + str(region) + '/bikeoutflow.h5') reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto') model.fit(x=x, y=y, epochs=100, validation_split=0.1, verbose = 0, shuffle=True, batch_size=128, callbacks=[reduce_lr]) model.save('model/' + str(region) + '/bikeoutflow.h5') print(str(region) + '-bikeoutflow:100 epoches have been trained over!') return def train4(region): # path = 'model/' + str(region) + '/taxiinflow.h5' # file = open(path, 'w') rawdata = pd.read_csv('model/' + str(region) + '/taxiinflow.csv').values if len(rawdata) == 0: print('Region' + str(region) + ' don\'t have data about taxiinflow!\nUse the unified model!') # return dic = makedata(region, 'model/' + str(region) + '/taxiinflow.csv') x = np.array(dic['x']) y = np.array(dic['y']) model = None if os.path.getsize('model/' + str(region) + '/taxiinflow.h5') == 0: model = Sequential() model.add(Dense(10, input_shape=(1,),activation='sigmoid', use_bias=True)) model.add(Dense(1, use_bias=True)) model.compile(loss=keras.losses.mean_squared_error, optimizer=keras.optimizers.RMSprop(0.1)) else: model = load_model('model/' + str(region) + '/taxiinflow.h5') reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto') model.fit(x=x, y=y, epochs=100, validation_split=0.1, verbose = 0, shuffle=True, batch_size=128, callbacks=[reduce_lr]) model.save('model/' + str(region) + '/taxiinflow.h5') print(str(region) + '-taxiinflow:100 epoches have been trained over!') return def train5(region): # path = 'model/' + str(region) + '/taxioutflow.h5' # file = open(path, 'w') rawdata = pd.read_csv('model/' + str(region) + '/taxioutflow.csv').values if len(rawdata) == 0: print('Region' + str(region) + ' don\'t have data about taxioutflow!\nUse the unified model!') # return dic = makedata(region, 'model/' + str(region) + '/taxioutflow.csv') x = np.array(dic['x']) y = np.array(dic['y']) model = None if os.path.getsize('model/' + str(region) + '/taxioutflow.h5') == 0: model = Sequential() model.add(Dense(10, input_shape=(1,),activation='sigmoid', use_bias=True)) model.add(Dense(1, use_bias=True)) model.compile(loss=keras.losses.mean_squared_error, optimizer=keras.optimizers.RMSprop(0.1)) else: model = load_model('model/' + str(region) + '/taxioutflow.h5') reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto') model.fit(x=x, y=y, epochs=100, validation_split=0.1, verbose = 0, shuffle=True, batch_size=128, callbacks=[reduce_lr]) model.save('model/' + str(region) + '/taxioutflow.h5') print(str(region) + '-taxioutflow:100 epoches have been trained over!') return def train(region = 0,all = False): if all == True: for i in range(1,863): # train1(i) train2(i) train3(i) train4(i) train5(i) print('Have trained all the models in the dataset!') return # train1(region) train2(region) train3(region) train4(region) train5(region) print('Finished the task to train region' + str(region) + '!') return ''' serve method ''' def serve(): demo = demonstrater() demo.run() return ''' 一个服务对象 ''' class demonstrater(): def __init__(self): self.predictvaluestaxiinflow = [Queue() for x in range(862)] self.realvaluestaxiinflow = [Queue() for x in range(862)] self.predictvaluestaxioutflow = [Queue() for x in range(862)] self.realvaluestaxioutflow = [Queue() for x in range(862)] self.predictvaluesbikeinflow = [Queue() for x in range(862)] self.realvaluesbikeinflow = [Queue() for x in range(862)] self.predictvaluesbikeoutflow = [Queue() for x in range(862)] self.realvaluesbikeoutflow = [Queue() for x in range(862)] self.timestamp = 1388505600 self.endtime = self.timestamp + 30 * 24 * 3600 self.id = 0 print('初始化完成!') return ''' 从时间戳获得日期 日期格式统一为%Y-%m-%d %H:%M:%S ''' def getdate(self): return time.strftime