SEED = 1234 random.seed(SEED) np.random.seed(SEED) torch.manual_seed(SEED) torch.cuda.manual_seed(SEED) torch.backends.cudnn.deterministic = True啥意思
时间: 2024-03-31 21:34:23 浏览: 16
这段代码是为了设置随机种子(seed),以保证程序的可重复性。在机器学习和深度学习中,随机数的使用非常普遍,比如在数据集的划分、模型参数的初始化等过程中都会用到随机数。但是每次运行程序时,随机数都是随机生成的,这样会导致每次的结果都不一样,不利于程序的调试和结果的复现。因此,设置随机种子可以使每次运行程序时生成的随机数都相同,从而保证程序的可重复性。其中,random.seed()、np.random.seed()、torch.manual_seed()、torch.cuda.manual_seed() 分别设置了 Python 内置随机数生成器、numpy 随机数生成器、PyTorch 的 CPU 随机数生成器、PyTorch 的 GPU 随机数生成器的随机种子。torch.backends.cudnn.deterministic = True 是为了保证 CUDA 卷积运算的可重复性。
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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.
用中文解释这段代码seed = 1 random.seed(seed) torch.manual_seed(seed) np.random.seed(seed) torch.cuda.manual_seed(seed)
这段代码用于设置随机数种子,保证每次运行程序时生成的随机数相同。seed = 1表示设置种子为1,random.seed(seed)表示设置Python自带的随机数生成器的种子为1,torch.manual_seed(seed)表示设置PyTorch的随机数生成器的种子为1,np.random.seed(seed)表示设置NumPy的随机数生成器的种子为1,torch.cuda.manual_seed(seed)表示设置PyTorch的CUDA随机数生成器的种子为1。通过设置种子,可以保证每次运行程序时生成的随机数一致,方便程序的调试和复现。