np.random.seed(1) torch.manual_seed(1) torch.cuda.manual_seed_all(1) torch.backends.cudnn.deterministic = True # 保证每次结果一样
时间: 2024-05-18 22:17:19 浏览: 94
这段代码的作用是设置随机数种子,以确保每次运行代码时生成的随机数结果是一样的。在机器学习和深度学习中,随机数种子的设置对于实验的可重复性和结果的稳定性非常重要。这段代码中,np.random.seed(1)、torch.manual_seed(1)和torch.cuda.manual_seed_all(1)分别设置了numpy和PyTorch在CPU和GPU上的随机数种子为1,而torch.backends.cudnn.deterministic = True则保证了使用CuDNN加速的计算结果也是一样的。
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seed = 1 random.seed(seed) torch.manual_seed(seed) np.random.seed(seed) torch.cuda.manual_seed(seed)
These lines of code are used to set the random seed for various libraries and modules used in a Python program.
The `seed` variable is set to a value of 1. This value can be changed to any integer to set a different seed.
The `random.seed(seed)` function call sets the seed for the Python `random` module. This module is used to generate random numbers in Python.
The `torch.manual_seed(seed)` function call sets the seed for the PyTorch library. PyTorch is a popular deep learning library.
The `np.random.seed(seed)` function call sets the seed for the NumPy library. NumPy is a library for numerical computing in Python.
Finally, the `torch.cuda.manual_seed(seed)` function call sets the seed for the PyTorch CUDA backend. This is used for GPU acceleration in PyTorch.
Setting the random seed ensures that the random numbers generated during program execution are consistent across different runs. This is useful for debugging and testing purposes.
用中文回答seed = 1234 np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed)
这段代码是设置随机数种子,可以保证每次运行代码生成的随机数都是相同的。其中,seed = 1234 是设置随机数种子的值;np.random.seed(seed) 是设置numpy库的随机数种子;torch.manual_seed(seed) 是设置pytorch的CPU随机数种子;torch.cuda.manual_seed(seed) 是设置pytorch的单个GPU随机数种子;torch.cuda.manual_seed_all(seed) 是设置pytorch的所有GPU随机数种子。