seed = 1234 np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed)

These lines of code set the random seed for three different libraries: numpy, PyTorch (CPU), and PyTorch (GPU). Setting the random seed ensures that the sequence of random numbers generated by these libraries is reproducible. This is useful when running experiments or training models, as it ensures that the results can be replicated.

The specific value used for the seed (1234 in this case) is arbitrary and could be any integer.

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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.

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

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。