本文整理汇总了Python中brain.Brain.train方法的典型用法代码示例。如果您正苦于以下问题：Python Brain.train方法的具体用法？Python Brain.train怎么用？Python Brain.train使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类brain.Brain的用法示例。
在下文中一共展示了Brain.train方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: train
# 需要导入模块: from brain import Brain [as 别名]
# 或者: from brain.Brain import train [as 别名]
def train(maxlens, steps, dropouts, dataset):
for code, text_file in dataset:
for maxlen in maxlens:
for step in steps:
for dropout in dropouts:
print '=========== TRAINING on: maxlen={}, step={}, lstm_size=128, dropout={}, text={}'.format(
maxlen,
step,
dropout,
text_file)
brain = Brain(maxlen=maxlen, lstm_size=128, dropout=dropout,
training_text=text_file)
brain.train(runid='11,{},tweets'.format(code), iterations=7, step=step)示例2: __init__
# 需要导入模块: from brain import Brain [as 别名]
# 或者: from brain.Brain import train [as 别名]
class Mediator:
"""Handles communication between objects."""
def __init__(self, look_ahead=4, skip_to_page=0, feats=10, max_q_sz=100,
base_url="http://www.xvideos.com/c/{0}/anal-12"):
# Let's set this up so gathering new videos can happen in the background.
self.scraped_videos = {}
gather_args = (look_ahead, skip_to_page, feats, max_q_sz)
self.gather_process = Thread(target = self.gather,
args = gather_args,
daemon = True)
self.scr = Scraper(base_url=base_url, pg_n=skip_to_page)
self.db = Database()
self.ai = Brain(self)
self.win = Window(self)
self.currently_loaded_video_data = {}
self.feats = feats
self.q = PriorityQueue(maxsize=max_q_sz)
self.lock = RLock()
if "brain.pkl" in os.listdir():
self.train()
self.get_next()
def start_gathering(self):
self.gather_process.start()
def get_next(self):
"""Determine which video should be selected, then update window."""
# Get the video likely to have the best rating
if self.q.qsize():
self.lock.acquire()
to_show_url = self.q.get()
self.lock.release()
scraped_data, preview_pic_url, guessed_rating = self.scraped_videos[to_show_url]
self.currently_loaded_video_data = scraped_data
pic1, pic2, pic3 = self.scr.load_pics(preview_pic_url)
self.win.update_images(pic1, pic2, pic3)
url = scraped_data["url"]
print("\n")
print("\ttitle:\t", scraped_data["title"])
print("\tlength:\t", scraped_data["duration"])
print("\tguessed rating:\t", guessed_rating)
print("\ttags:\t", " ".join(scraped_data["tags"]))
print()
else:
print("Error: still gathering videos.")
def gather(self, look_ahead, skip_to_page, feats, max_q_sz):
""" Add videos to the priority queue independently
of rating and loading videos. """
while True:
if self.q.qsize() < max_q_sz:
for i in range(look_ahead):
self.scr.next()
if self.db.has_video(self.scr.cur_vid):
# make sure we only work on unrated videos
print(".", end="")
continue
scraped_data = self.scr.scrape_video()
pred_rating = self.ai.predict(scraped_data)
self.scraped_videos[self.scr.cur_vid] = (scraped_data,
self.scr.cur_pic,
pred_rating)
self.lock.acquire()
self.q.put(self.scr.cur_vid, -pred_rating)
self.lock.release()
print(":", end="")
def save(self, rating):
"""Save the data scraped from the current video then get next video."""
self.currently_loaded_video_data["loved"] = rating
sys.stdout.flush()
self.db.save(self.currently_loaded_video_data)
self.get_next()
def open_vid(self):
webbrowser.open(self.scr.cur_vid)
def train(self):
tags, ratings, tag_to_vec = self.db.vectorize_tags(self.feats)
self.ai.train(tags, ratings, tag_to_vec)
def close_db(self):
"""When a window closes, disconnect from a database."""
self.db.cnx.close()