importnumpyasnpimportrandomimportosimporttorchdefseed_torch(seed=1029):random.seed(seed)os.environ['PYTHONHASHSEED']=str(seed)np.random.seed(seed)torch.manual_seed(seed)torch.cuda.manual_seed(seed)torch.cuda.manual_seed_all(seed)# if you are using multi-GPU.torch.backends.cudnn.benchmark=Fal...
Before diving into the problem, it is essential to have a basic understanding of the concept of random seeds in machine learning. Random seeds are used to initialize the random number generator, ensuring reproducibility of experiments. By setting a specific seed value, we can ensure that the ran...
在tensorflow 中,随机操作依赖于两个不同的种子:一个全局种子,由tf.set_random_seed设置,一个操作...
import numpy as np import tensorflow as tf global_seed = 42 N_chains = 5 np.random.seed(global_seed) seeds = np.random.randint(0, 4294967295, size=N_chains) for i in range(N_chains): tf.set_random_seed(seeds[i]) ... some stuff ... kernel_initializer = tf.random_normal_initiali...
pprint.pprint(cfg)ifnotargs.randomize:# fix the random seeds (numpy and caffe) for reproducibilitynp.random.seed(cfg.RNG_SEED) caffe.set_random_seed(cfg.RNG_SEED)print'Setting GPU device %d for training'% cfg.GPU_ID caffe.set_mode_gpu() ...
.option(None,"--fix_random_seeds", action="store_true", default=False) .option(None,"--runs", type='int', default=1) .option(None,"--verbose", action="store_true", default=False) ).process(args=sys.argv[1:])ifcommand_line.options.fix_random_seeds: ...
To generate different sequences across sessions, set neither graph-level nor op-level seeds: a = tf.random_uniform([1]) b = tf.random_normal([1]) print("Session 1") withtf.Session()assess1: print(sess1.run(a))# generates 'A1' ...
python编程之random.seed()的用法 random是随机数的意思,seed是种子的意思。 百度查明 还是不懂,什么是种子? 我们来看看案例 案例一、输出种子的值 案例二、 打印种子之后生成一个随机数的 不同的种子,生成的数不一样; ... set的用法 set内部使用红黑树实现自动有序且去重 set指令: set,顾名思义,就是数学...
My environment: python2.7, cuda8.0, cudnn, pytorch 0.3.1 I set all random seeds but I still can't reproduce results. Here is part of my code: torch.manual_seed(0) torch.cuda.manual_seed(0) np.random.seed(0) transform_train = transforms.C...
That only sets the graph-level random seed. If you execute this snippet several times in a row, the graph will change, and two shuffle statements will get different operation-level seeds. The details are described in the doc string for set_random_seed To get deterministic a_shuf you can ...