def main():
#args = argparse.ArgumentParser().parse_args()
args = argumentparser.ArgumentParser()
if args.run_all == 1:
# Try all combinations
#models = ['cnn-rand', 'cnn-static', 'cnn-non-static', 'cnn-3']
models = ['cnn-3', 'cnn-non-static', 'cnn-static','cnn-rand']
#data_dirs = ['data/', '/home/fanyy/sohu/raw_data/','/home/fanyy/sohu/raw_data_short/', '/home/fanyy/sohu/raw_data_short/stopwords/']
data_dirs = ['data/', '/home/fanyy/sohu/raw_data/','/home/fanyy/sohu/raw_data_short/']
nb_words = [10000]
max_sequence_len = [200, 500]
batch_size = [64]
for model in models:
for data_dir in data_dirs:
for nb in nb_words:
for max_len in max_sequence_len:
for b_size in batch_size:
args.model_name = model
args.data_dir = data_dir
args.nb_words = nb
args.max_sequence_len = max_len
args.batch_size = b_size
try:
run(args)
except:
print("exception!!!")
else:
print("OK!")
else:
run(args)
def __init__(self):
"""Get command line arguments to check which mode is enabled
"""
args = argumentparser.ArgumentParser().parse()
pws = list()
config = configreader.ConfigReader().read()
self.source_port = int(config['NSDP']['SourcePort'])
self.dest_port = int(config['NSDP']['DestPort'])
self.interface = config['NSDP']['Interface']
self.dest_ip = config['NSDP']['DestIP']
self.delay = config.getfloat('NSDP', 'Delay')
self.network = network.Network(self.interface, self.dest_ip,
self.source_port, self.dest_port)
self.quiet = False
if args['sniffer'] == True:
self.mode = 'sniffer'
elif args['discover'] == True:
self.mode = 'discover'
if args["target"] is not None:
self.discovermode = "target"
self.target = args["target"][0]
self.fd = None
else:
self.discovermode = "targetlist"
self.fd = args["targetlist"][0]
if args["delay"] is not None:
self.delay = args["delay"][0]
elif args['setpassword'] == True:
self.mode = 'setpassword'
self.oldpassword = args["currentpassword"][0]
self.newpassword = args["newpassword"][0]
self.macaddress = args["macaddress"][0]
elif args['reboot'] == True:
self.mode = 'reboot'
self.password = args['password'][0]
self.macaddress = args['macaddress'][0]
elif args['bruteforce'] is not None:
self.mode = 'bruteforce'
self.fd = args['bruteforce'][0]
self.password = args['newpassword'][0]
self.macaddress = args['macaddress'][0]
if args['quiet'] == True:
self.quiet = True
def __init__(self):
self.argumentParser = ap.ArgumentParser(self)
self.fileFinder = ff.FileFinder(self)
self.inputs = 1
self.nLayers = self.argumentParser.nLayers()
self.nNodes = self.argumentParser.nNodes()
self.outputs = 1
self.networkType = self.argumentParser.type()
self.network = nn.NeuralNetwork(self)
self.network.constructNetwork(inputs=self.inputs,
nNodes=self.nNodes,
nLayers=self.nLayers,
outputs=self.outputs,
networkType=self.networkType)
self.saver = ckps.CheckpointSaver(self, self.argumentParser().save)
self.networkTrainer = nt.NetworkTrainer(self, self.saver)
self.function = lambda r: r / r * np.random.normal(
0, 1) # +np.sin(7.0*np.pi*r)
self.function = lambda r: 1 / r**12 - 1 / r**6
self.function = lambda r: 4 * (1.0 / (r**12) - 1.0 /
(r**6)) - 4 * (1.0 / (2.5**12) - 1.0 /
(2.5**6))
self.dataGenerator = gen.DataGenerator(0.87, 2.5, self)
self.dataGenerator.setFunction(self.function)
if not self.argumentParser().file == None:
self.dataGenerator.setGeneratorType("file")
else:
self.dataGenerator.setGeneratorType("function")
#self.dataGenerator.setGeneratorType("VMC")
#self.dataGenerator.setGeneratorType("noise")
self.dataSize = int(9987)
self.numberOfEpochs = int(100)
self.batchSize = int(500)
self.testSize = self.dataSize #int(600)
self.testInterval = 5000
self.printer = printer.Printer(self)
self.printer.printSetup()
self.plotter = plotter.Plotter(self)
import sys
sys.path.append("../CBOW_HS/")
from cbow_hs_model import CBOWModel
from input_data import InputData
import torch.optim as optim
from tqdm import tqdm
import argumentparser as argumentparser
args = argumentparser.ArgumentParser()
# hyper parameters
WINDOW_SIZE = args.window_size # 上下文窗口c
BATCH_SIZE = args.batch_size # mini-batch
MIN_COUNT = args.min_count # 需要剔除的 低频词 的频
EMB_DIMENSION = args.embed_dimension # embedding维度
LR = args.learning_rate # 学习率
NEG_COUNT = args.neg_count # 负采样数
EPOCH = args.epoch
class Word2Vec:
def __init__(self, input_file_name, output_file_name):
self.output_file_name = output_file_name
self.data = InputData(input_file_name, MIN_COUNT)
self.model = CBOWModel(self.data.word_count, EMB_DIMENSION).cuda()
self.lr = LR
self.optimizer = optim.SGD(self.model.parameters(), lr=self.lr)
def train(self):
for _ in range(1, EPOCH + 1):