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266 def seed(self): 267 import random 268 import numpy as np 269 import tensorflow as tf 270 self.flags.seed = self.flags.seed or np.random.randint(1000000) 271 random.seed(self.flags.seed) 272 np.random.seed(self.flags.seed)

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680 def init_random(self): 681 682 """ 683 desc: 684 Initializes the random number generators. For some reason, the numpy 685 random seed is not re-initialized when the experiment is started 686 again with the multiprocess runner, resulting in identical random 687 runs. The standard random module doesn't suffer from this problem. 688 But to be on the safe side, we now explicitly re-initialize the 689 random seed. 690 691 See also: 692 693 - <http://forum.cogsci.nl/index.php?p=/discussion/1441/> 694 """ 695 696 import random 697 random.seed() 698 try: 699 # Don't assume that numpy is available 700 import numpy 701 numpy.random.seed() 702 except: 703 pass

24 def set_random_seed(seed): 25 # set Python random seed 26 random.seed(seed) 27 # set NumPy random seed 28 np.random.seed(seed)

31 def __init__(self, seed): 32 self._seed = seed 33 self._master_rng_fct = rand.RandomState(seed) 34 self._master_rng = lambda: self._master_rng_fct.randint(1, 2 ** 16)

36 def set_seed_random(seed): 37 random.seed(seed) 38 np.random.seed(seed) 39 if chainer.cuda.available: 40 chainer.cuda.cupy.random.seed(seed)