其实,第二遍的np.random.rand(10)已经不是在之前设置的np.random.seed(0)下了,所以第二遍的随机数组只是在默认random下随机挑选的样本数值。 那如何让两次随机数组一样? 只需要再输入一遍np.random.seed(0)。 np.random.seed(0) a = np.random.rand(4,3) np.random.seed(0) b = np.random.rand(4,...
opt.seed = random.randint(1, 10000) print("Random Seed: ", opt.seed) torch.manual_seed(args.seed) # 为CPU设置随机种子 if cuda: torch.cuda.manual_seed(seed) # 为当前GPU设置随机种子 torch.cuda.manual_seed_all(seed)# 为所有GPU设置随机种子 到此这篇关于Numpy的np.random随机模块详解的文章...
其实,第二遍的np.random.rand(10)已经不是在之前设置的np.random.seed(0)下了,所以第二遍的随机数组只是在默认random下随机挑选的样本数值。 那如何让两次随机数组一样? 只需要再输入一遍np.random.seed(0)。 np.random.seed(0) a = np.random.rand(4,3) np.random.seed(0) b = np.random.rand(4,...
opt.seed = random.randint(1, 10000)print("Random Seed: ", opt.seed) torch.manual_seed(args.seed)# 为CPU设置随机种子ifcuda: torch.cuda.manual_seed(seed)# 为当前GPU设置随机种子torch.cuda.manual_seed_all(seed)# 为所有GPU设置随机种子 到此这篇关于Numpy的np.random随机模块详解的文章就介绍到这...
简介:Numpy | np.random随机模块的使用介绍 平时都会使用到随机模块,一般是torch.random或者是numpy.random,有或者是直接使用ramdom这个python内置的工具包,那么下面就简单记录一下numpy.random常用的函数。 1. 随机抽样 import numpy as npnp.random.randn(3,3) # 从标准正太分布中返回样本np.random.rand(3,3) ...
np.random.seed(args.seed) torch.manual_seed(args.seed) if args.cuda: torch.cuda.manual_seed(args.seed)log = None # Save model and meta-data. Always saves in a new folder. if args.save_folder: exp_counter = 0 save_folder = '{}/exp{}/'.format(args.save_folder, exp_counter)...
sampler) def seed_worker(worker_id): """设置数据加载器工作线程的随机种子,以确保可重复性。""" worker_seed = torch.initial_seed() % 2 ** 32 np.random.seed(worker_seed) random.seed(worker_seed) def build_yolo_dataset(cfg, img_path, batch, data, mode='train', rect=False, stride=32...
Namespace): print(args) random.seed(args.seed) # Synthesize a prompt with the given input length. tokenizer = AutoTokenizer.from_pretrained( args.tokenizer, trust_remote_code=args.trust_remote_code) prompt = f"Generate an example of a user profile given the following schema: {json.dumps(...
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() torch.manual_seed(args.seed) vae = VAE(args.z_dim) optimizer = Adam(vae.parameters(), lr=args.lr) device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') vae.to(device) # download mnist & setup loaders if args.mode == "train": train_set = ...