def train(_, network, lock, loss_record):
game_index = network.count()
print('{} game start'.format(game_index))
tree = mcts.MCTS(network, game_index)
game_begin_time = int(time.time())
tree.game()
learn_begin_time = int(time.time())
lock.acquire()
print('{} learn start'.format(game_index))
mse_total, cross_entropy_total = 0, 0
np.random.seed(int.from_bytes(os.urandom(4), byteorder='little'))
for _ in range(TRAINING_STEP):
mse, cross_entropy = network.learn()
mse_total += mse
cross_entropy_total += cross_entropy
loss_record.add(
[mse_total / TRAINING_STEP, cross_entropy_total / TRAINING_STEP])
print([mse_total / TRAINING_STEP, cross_entropy_total / TRAINING_STEP])
if loss_record.size() % SAVE_INTERVAL == 0:
print('save')
network.save_state("{}state_{}.pkl".format(STATE_SAVE_FOLDER,
loss_record.size()))
network.save_memory("memory.npy")
loss_record.save("loss_record.pkl")
print('{} learn end'.format(game_index))
lock.release()
print('{} game end'.format(game_index))
learn_end_time = int(time.time())
learn_min, learn_sec = utils.compute_time(learn_begin_time, learn_end_time)
print('learning cost {} mins {} seconds'.format(learn_min, learn_sec))
game_end_time = int(time.time())
game_min, game_sec = utils.compute_time(game_begin_time, game_end_time)
print('{} game cost {} mins {} seconds'.format(game_index, game_min,
game_sec))
def recent_activities(self):
context = aq_inner(self.context)
for brain in self._data():
activity = brain[1]
yield dict(time=compute_time(int(time.time()) - brain[0]),
action=activity['action'],
user=activity['user'],
user_url="%s/author/%s" % (context.portal_url(), activity['user']),
object=activity['object'],
object_url=activity['object_url'],
parent=activity['parent'],
parent_url=activity['parent_url'],
)
def recent_activities(self):
context = aq_inner(self.context)
activities = getUtility(IRecentActivityUtility)
return [ dict(time=compute_time(int(time.time()) - activity[0]),
action=activity[1]['action'],
user=activity[1]['user'],
user_url="%s/author/%s" % (context.portal_url(), activity[1]['user']),
object=activity[1]['object'],
object_url=activity[1]['object_url'],
parent=activity[1]['parent'],
parent_url=activity[1]['parent_url'],
)
for activity in activities.getRecentActivity(100)
]
def recent_activities(self):
"""Recent activities, most recent activities come first.
"""
context = aq_inner(self.context)
for brain in self._data():
activity = brain[1]
fullname = ''
if 'fullname' in activity:
fullname = activity['fullname']
yield dict(time=compute_time(int(time.time()) - brain[0]),
action=activity['action'],
user=activity['user'],
fullname=fullname,
user_url="%s/author/%s" % (context.portal_url(), activity['user']),
object=activity['object'],
object_url=activity['object_url'],
parent=activity['parent'],
parent_url=activity['parent_url'],
)
