for each in productLinkedList:
print('|', end='')
ll.append(getInfo(each[0], each[3], each[1]))
#saveAsCSV(ll, "上海数据开放")
#ll = []
for each in appLinkedList:
print('|', end='')
ll.append(getInfo(each[0], each[3], each[1]))
#saveAsCSV(ll, "上海应用开放")
print('抓取数据成功')
#classList = []
#for each in ll:
# temp = EntryData()
# temp.setFromString(each[-2],each[1], each[0], 'SH')
# classList.append(temp.getData())
classList = []
for each in infoList:
temp = Data(each[2], each[1], each[0], 'BJ')
classList.append(temp)
print('数据分析中...')
saveAsPKL(classList, './data/SHEntry')
print('数据保存成功')
#保存数据连接条目
#saveAsPickle("shanghaiProduct.pkl", productLinkedList)
#saveAsPickle("shanghaiApp.pkl", appLinkedList)
#saveAsPickle("shanghaiInterface.pkl", interfaceLinkedList)
from dataClass import Data
from dataClass import ShangHai
a = Data("中国", "城市建设", "宅基地数据")
a.printInfo()
b = ShangHai("中国", "城市建设", "宅基地数据", "妈妈")
b.printInfo()
tf.compat.v1.disable_v2_behavior()
filePath = Path("C:/Users/soumi/Documents/Train-Images-All/").glob('*.jpg')
jsonPath = Path("C:/Users/soumi/Documents/Train-Images-All/trainData.txt")
savePath = "C:/Users/soumi/Documents/VAE-model/model/"
trainbatchSize = 16
valbatchSize = 16
epochs = 101
channelSize = 32
loss = 'ce_kldivergence'
learningRate = 0.0001
outputTensorName = "Inference/Output"
imageHeight = 240
imageWidth = 320
trainingLossList = list()
validationLossList = list()
data = Data(filePath, jsonPath)
data.jsonData()
trainData = data.loadLabels()
print ("train data shape is", trainData.shape)
def getnumberofBatches(Datasize, batchSize):
return int(Datasize/batchSize)
gpuInitialised = True
'''
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
# Restrict TensorFlow to only use the first GPU
try:
tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
logical_gpus = tf.config.experimental.list_logical_devices('GPU')