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):