def __get_text(self, chapter_start_url, book_name, topic):
'''
小说正文爬取并写入磁盘
:param chapter_start_url:
:param book_name:
:param topic:
:return:
'''
# 文章正文部分是js 用selenium模拟浏览器获取html页面
driver = dr()
driver.get(chapter_start_url)
# 网络时整个页面没有加载外全会导致抓取内容错误
# 直接调用sheep对整个线程不安全 故采用 ▲t1-▲t2的方式拖延程序
start_time = int(time.time())
while True:
end_time = int(time.time())
if end_time - start_time >= 1:
break
soup = BeautifulSoup(driver.page_source, "html5lib")
ps = soup.select("#chapter-content p")
text_list = [
"{0}\n".format(p.string.encode("utf-8")) for p in ps if p.string
]
driver.quit()
io = IO()
io.write(text_list, book_name, topic)
logging.info(
"--------------------{0}{1} ..下载完成--------------------".format(
book_name.encode("utf-8"), topic))
def __init__(self, number_of_epochs=1000, content_weight=1e3, style_weight=1e-2, model_name="mobilenetv2_coco_voctrainaug", enable_gpu=False, verbose=False):
self.number_of_epochs = number_of_epochs
self.content_weight = content_weight
self.style_weight = style_weight
self.content_layers = ["block5_conv2"]
self.style_layers = ["block1_conv1", "block2_conv1", "block3_conv1", "block4_conv1", "block5_conv1"]
self.model_name = model_name
self.verbose = verbose
self.enable_gpu = enable_gpu
self._set_device()
tf.compat.v1.enable_eager_execution()
self.path = IO()
if(self.verbose == True):
print ("Eager Execution: {}".format(tf.executing_eagerly()))
parser.add_argument('--MUTPB', type=float, default=0.1)
parser.add_argument('--gain_max', type=float, default=2.0)
parser.add_argument('--speed_max', type=float, default=2.0)
args = parser.parse_args()
env_name = args.env_name
env = gym.make(env_name)
log_name = 'PSO4_open'
# Set the logging variables
# This also creates a new log file
# Create log files
log_dir = configure_log_dir(env_name, txt=log_name, No_time=False)
logging_output(log_dir)
logger = LoggerCsv(log_dir, csvname='log_results')
results_IO = IO(os.path.join(log_dir, 'results.pkl'))
args_IO = IO(os.path.join(log_dir, 'args.pkl')).to_pickle(args)
def parmeter_generate(pmin, pmax):
parm_list = [random.uniform(pmin, pmax) for _ in range(27)]
return parm_list
def generate(size, pmin, pmax, smin, smax):
part = creator.Particle(parmeter_generate(pmin, pmax))
part.speed = [random.uniform(smin, smax) for _ in range(size)]
part.smin = smin
part.smax = smax
return part
particles_num = 22
elif task_mode == '5_sin':
from CPG_core.butterfly_osc.butterfly_oscillator_5_sin import oscillator_nw
particles_num = 29
else:
raise print('task mode does not exist')
else:
raise print("env :{} task does not implemented.".format(env_name))
results_dir = os.path.join(results_path, 'results')
monitor_dir = os.path.join(results_dir, 'monitor')
results = IO(os.path.join(results_path, 'results.pkl')).read_pickle()
#plot
# gen = len(results)
# fitness =[]
# for g in range (1, gen+1):
# fitness.append(results['gen{}'.format(g)]['best_fitness'])
#
# fitness =np.array(fitness)
# gen_x = np.linspace(1,30,30)
# plt.plot(gen_x, fitness)
# plt.show()
#
# if not os.path.isdir(results_dir):
# os.makedirs(results_dir) # create path
# if not os.path.isdir(monitor_dir):
def __init__(self, model_name, verbose):
self.model_name = model_name
self.verbose = verbose
self.path = IO()