torch随机
torch.random.fork_rng(devices=None, enabled=True, _caller='fork_rng', _devices_kw='devices')¶
分叉 RNG,以便在您返回时将 RNG 重置为之前的状态。
参数
-
设备(可迭代的 CUDA ID 的_)–派生 RNG 的 CUDA 设备。 CPU RNG 状态始终为分叉。 默认情况下,
fork_rng()
可在所有设备上运行,但是如果您的计算机上有很多设备,则将发出警告,因为在这种情况下此功能运行非常缓慢。 如果您明确指定设备,该警告将被取消_ -
启用的 (bool )–如果
False
,则不分叉 RNG。 这是一个方便的参数,用于轻松禁用上下文管理器,而不必删除它并取消其下的 Python 代码的缩进。
torch.random.get_rng_state()¶
以 <cite>torch.ByteTensor</cite> 的形式返回随机数生成器状态。
torch.random.initial_seed()¶
返回长为 Python <cite>long</cite> 的用于生成随机数的初始种子。
torch.random.manual_seed(seed)¶
设置用于生成随机数的种子。 返回一个<cite>torch.生成器</cite>对象。
Parameters
种子 (python:int )–所需的种子。
torch.random.seed()¶
将用于生成随机数的种子设置为不确定的随机数。 返回用于播种 RNG 的 64 位数字。
torch.random.set_rng_state(new_state)¶
设置随机数生成器状态。
Parameters
new_state (torch.ByteTensor )–所需状态
随机数发生器
torch.random.get_rng_state()
Returns the random number generator state as a <cite>torch.ByteTensor</cite>.
torch.random.set_rng_state(new_state)
Sets the random number generator state.
Parameters
new_state (torch.ByteTensor) – The desired state
torch.random.manual_seed(seed)
Sets the seed for generating random numbers. Returns a <cite>torch.Generator</cite> object.
Parameters
seed (python:int) – The desired seed.
torch.random.seed()
Sets the seed for generating random numbers to a non-deterministic random number. Returns a 64 bit number used to seed the RNG.
torch.random.initial_seed()
Returns the initial seed for generating random numbers as a Python <cite>long</cite>.
torch.random.fork_rng(devices=None, enabled=True, _caller='fork_rng', _devices_kw='devices')
Forks the RNG, so that when you return, the RNG is reset to the state that it was previously in.
Parameters
-
devices (iterable of CUDA IDs) – CUDA devices for which to fork the RNG. CPU RNG state is always forked. By default,
fork_rng()
operates on all devices, but will emit a warning if your machine has a lot of devices, since this function will run very slowly in that case. If you explicitly specify devices, this warning will be suppressed -
enabled (bool) – if
False
, the RNG is not forked. This is a convenience argument for easily disabling the context manager without having to delete it and unindent your Python code under it.