def get_model(): """ 获取模型,并加载官方预训练的模型参数 """ # 获取模型 model = models.main() # model.summary() cache_subdir='models_dir' # 下载模型参数 WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5' filename = 'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5' # 下载后保存的文件名 checksum = '3e9f4e4f77bbe2c9bec13b53ee1c2319' weights_path = get_file(filename, WEIGHTS_PATH_NO_TOP, cache_subdir='models') # print(weights_path) # 加载参数 model.load_weights(weights_path, by_name=True) # 编译 model.compile(loss=customied_loss, optimizer=Adam(1e-3), metrics=['accuracy']) return model
def get_model(): """ 加载模型和参数""" # 获取模型 model = models.main() # 加载参数 # model.load_weights("callbacks/ep044-loss0.030-val_loss0.028.h5") model.load_weights('.\callbackslast1.h5') return model
def main(args): #os.listdir(data_root) img_shape = (args.channels, args.img_size, args.img_size) cuda = True if torch.cuda.is_available() else False # Loss function adversarial_loss = torch.nn.BCELoss() auxiliary_loss = torch.nn.CrossEntropyLoss() # Initialize generator and discriminator generator, discriminator = models.main(args) if cuda: generator.cuda() discriminator.cuda() adversarial_loss.cuda() auxiliary_loss.cuda() generator.apply(weights_init_normal) discriminator.apply(weights_init_normal) print('Generator: ', generator) print('Discriminator: ', discriminator) dataset = datasets.ImageFolder(root=args.data_root, transform=transforms.Compose([ transforms.Resize(args.img_size), transforms.CenterCrop(args.img_size), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ])) # Create the dataloader dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=True, num_workers=2) train(generator, discriminator, dataloader, args, cuda, adversarial_loss, auxiliary_loss)
import batch import flagfresher import os import models #os.system('/bin/bash -c "docker volume rm $(docker volume ls -qf dangling=true)"') # docker volume rm $(docker volume ls -qf dangling=true) teams = 10 models.main(teams) batch.start_awd() flagfresher.main()
def sync(): import models models.main()
thrds = 8 # docker 同时操作线程 npcteams = 2 #额外的npc队伍 #lock = threading.Lock() q = Queue() logger = logset('start') logger.addHandler(console) models.main(npcteams) #初始化数据库 timespan = 1 * 60 # 刷新 flag 时间 ''' 主要流程 先创建容器,然后启动容器,再关闭容器,等比赛开始时再启动容器 ''' # port 规则为 3 00 队伍id 22 服务端口 subject = { #'yunnam_simple': {'sshport':30022,'serviceport':30080}, #'pwn_simple': {'sshport':30032,'serviceport':30090}, #'tomcat8': {'sshport':30042,'serviceport':30040}, 'pwn':[{'network':'172.10.%d.1','servicename':'awd_note','serviceport':44500}]
def main(): # Filter warnings that polute the project stdout. filter_warnings() # Rationale: produce cleaner results. # Set the random seed for the entire project. du.set_random_seed(0) # Rationale: ensure reproducibility of the results. # Flush previous runs. # constants.flush_project_results(constants.TMP_PATH, # constants.OUTPUT_PATH) # Rationale: provide a clear state for the project to run and enforces # reproducibility of the results. # Download, load and save data. data_loading.main() dataframe = data_loading.load_data(constants.DATASET_PATH, constants.TMP_PATH) data_loading.save_data(dataframe, constants.TMP_PATH, constants.DATASET_PATH) # Rationale: *Loading*: load data in the main module and pass it as a first # argument to every other defined function (that relates to the data set) # thus saving precious time with data loading. *Saving*: for big data sets # saving the dataset as a fast read format (such as HDF5) saves time. # Load and combine data processing pipelines. data_processing.main(dataframe, nan_strategy='drop') # Rationale: prepare data to be fed into the models. # Different algorithms make use of different data structures. For instance # XGBoost allow for nans. Data transformations usually don't. # Perform exploratory data analyses. data_exploration.main(dataframe) # Rationale: conduct exploratory data analyses. # Data split. # Removed. # Rationale: module 'models' should execute this. # Perform grid search. # Iteration over processed data sets may occur here since they are model # dependent. grid_search.main(constants.MODELS, constants.GRIDS) best_combination_of_datasets_and_grids = ( grid_search.dict_of_best_datasets_and_grids(constants.MODELS, constants.GRIDS)) best_datasets = best_combination_of_datasets_and_grids['best_datasets'] best_grids = best_combination_of_datasets_and_grids['best_grids'] # Rationale: perform grid search as part of machine learning best # practices. # Summary of what was executed so far: # 1) Setting of the random seed for reproducibility. # 2) Flusing of intermediate results for a clean run. # 3) Data loading and data saving. # 4) Conduction of exploratory data analyses. # 5) Grid search of best model hyper parameters. # To conclude our project we need the grand finale: model selection and # evaluation/comparison. models.main(constants.MODELS, best_datasets, best_grids, constants.MODEL_FITTING_PARAMETERS)
import config import lee lee.connect(config.mysql_path) from models import main main()
import settings import models import responder from handlers import WineAttributeResource, PredictionResource, HealthCheckResource models.main() api = responder.API(cors=True, allowed_hosts=["*"], cors_params={ "allow_origins": "*", "allow_methods": "*", "allow_headers": "*" }) api.add_route('/api/wine_attributes', WineAttributeResource) api.add_route('/api/predict', PredictionResource) api.add_route('/api/healthcheck', HealthCheckResource) if __name__ == '__main__': api.run(address="0.0.0.0", port=5432, debug=True)
# This file is the master python script, # that will run each of the parsers and populate the database. ############################### # Env # import models import config import gffProcessor import sqlA_insert as insert import vcf_parse ############################### # Main # if __name__ == "__main__": #parse the gff and populate the database print "Creating database\n" models.main() print "Load GFF into gene_model database\n" gffProcessor.main(config.GFF, config.CHROMOSOMES) print "Load the interaction network into database\n" insert.main() #print "Populate the VCF tables" #vcf_parse.main()